Professional-Data-Engineer Google Professional Data Engineer Exam Questions and Answers

Questions 4

Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow. Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour.

The data scientists have written the following code to read the data for a new key features in the logs.

BigQueryIO.Read

.named(“ReadLogData”)

.from(“clouddataflow-readonly:samples.log_data”)

You want to improve the performance of this data read. What should you do?

Options:

Specify the TableReference object in the code.

Use .fromQuery operation to read specific fields from the table.

Use of both the Google BigQuery TableSchema and TableFieldSchema classes.

Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.

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Questions 5

You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?

Options:

Make a call to the Stackdriver API to list all logs, and apply an advanced filter.

In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.

In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.

Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.

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Questions 6

Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other’s data. You want to ensure appropriate access to the data. Which three steps should you take? (Choose three.)

Options:

Load data into different partitions.

Load data into a different dataset for each client.

Put each client’s BigQuery dataset into a different table.

Restrict a client’s dataset to approved users.

Only allow a service account to access the datasets.

Use the appropriate identity and access management (IAM) roles for each client’s users.

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Questions 7

You are troubleshooting your Dataflow pipeline that processes data from Cloud Storage to BigQuery. You have discovered that the Dataflow worker nodes cannot communicate with one another Your networking team relies on Google Cloud network tags to define firewall rules You need to identify the issue while following Google-recommended networking security practices. What should you do?

Options:

Determine whether your Dataflow pipeline has a custom network tag set.

Determine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 for the Dataflow network tag.

Determine whether your Dataflow pipeline is deployed with the external IP address option enabled.

Determine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 on the subnet used by Dataflow workers.

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Answer:

Explanation:

Dataflow worker nodes need to communicate with each other and with the Dataflow service on TCP ports 12345 and 12346. These ports are used for data shuffling and streaming engine communication. By default, Dataflow assigns a network tag called dataflow to the worker nodes, and creates a firewall rule that allows traffic on these ports for the dataflow network tag. However, if you use a custom network tag for your Dataflow pipeline, you need to create a firewall rule that allows traffic on these ports for your custom network tag. Otherwise, the worker nodes will not be able to communicate with each other and the Dataflow service, and the pipeline will fail.

Therefore, the best way to identify the issue is to determine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 for the Dataflow network tag. If there is no such firewall rule, or if the firewall rule does not match the network tag used by your Dataflow pipeline, you need to create or update the firewall rule accordingly.

Option A is not a good solution, as determining whether your Dataflow pipeline has a custom network tag set does not tell you whether there is a firewall rule that allows traffic on the required ports for that network tag. You need to check the firewall rule as well.

Option C is not a good solution, as determining whether your Dataflow pipeline is deployed with the external IP address option enabled does not tell you whether there is a firewall rule that allows traffic on the required ports for the Dataflow network tag. The external IP address option determines whether the worker nodes can access resources on the public internet, but it does not affect the internal communication between the worker nodes and the Dataflow service.

Option D is not a good solution, as determining whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 on the subnet used by Dataflow workers does not tell you whether the firewall rule applies to the Dataflow network tag. The firewall rule should be based on the network tag, not the subnet, as the network tag is more specific and secure. References: Dataflow network tags | Cloud Dataflow | Google Cloud, Dataflow firewall rules | Cloud Dataflow | Google Cloud, Dataflow network configuration | Cloud Dataflow | Google Cloud, Dataflow Streaming Engine | Cloud Dataflow | Google Cloud.

Questions 8

You need to create a new transaction table in Cloud Spanner that stores product sales data. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?

Options:

The current epoch time

A concatenation of the product name and the current epoch time

A random universally unique identifier number (version 4 UUID)

The original order identification number from the sales system, which is a monotonically increasing integer

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Questions 9

Your company operates in three domains: airlines, hotels, and ride-hailing services. Each domain has two teams: analytics and data science, which create data assets in BigQuery with the help of a central data platform team. However, as each domain is evolving rapidly, the central data platform team is becoming a bottleneck. This is causing delays in deriving insights from data, and resulting in stale data when pipelines are not kept up to date. You need to design a data mesh architecture by using Dataplex to eliminate the bottleneck. What should you do?

Options:

1. Create one lake for each team. Inside each lake, create one zone for each domain.2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.3. Have the central data platform team manage all zones' data assets.

1 Create one lake for each team. Inside each lake, create one zone for each domain.2. Attach each to the BigQuory datasets created by the individual teams as assets to the respective zone.3. Direct each domain to manage their own zone's data assets.

1 Create one lake for each domain. Inside each lake, create one zone for each team.2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.3. Direct each domain to manage their own lake's data assets.

1 Create one lake for each domain. Inside each lake, create one zone for each team.2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.3. Have the central data platform team manage all lakes' data assets.

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Questions 10

You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?

Options:

Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.

Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.

Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

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Questions 11

You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time. What should you do?

Options:

Send the data to Google Cloud Datastore and then export to BigQuery.

Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.

Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.

Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.

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Questions 12

Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?

Options:

Assign global unique identifiers (GUID) to each data entry.

Compute the hash value of each data entry, and compare it with all historical data.

Store each data entry as the primary key in a separate database and apply an index.

Maintain a database table to store the hash value and other metadata for each data entry.

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Questions 13

Your company’s customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations. The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations. What should you do?

Options:

Add a node to the MySQL cluster and build an OLAP cube there.

Use an ETL tool to load the data from MySQL into Google BigQuery.

Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.

Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.

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Questions 14

You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

Options:

Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.

Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.

Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use thedefault autoscaling setting for worker instances.

Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.

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Questions 15

You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

Options:

Deploy the Cloud SQL Proxy on the Cloud Dataproc master

Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet

Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter

Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role

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Questions 16

You are migrating your on-premises data warehouse to BigQuery. As part of the migration, you want to facilitate cross-team collaboration to get the most value out of the organization's data. You need to design an architecture that would allow teams within the organization to securely publish, discover, and subscribe to read-only data in a self-service manner. You need to minimize costs while also maximizing data freshness What should you do?

Options:

Create authorized datasets to publish shared data in the subscribing team's project.

Create a new dataset for sharing in each individual team's project. Grant the subscribing team the bigquery. dataViewer role on thedataset.

Use BigQuery Data Transfer Service to copy datasets to a centralized BigQuery project for sharing.

Use Analytics Hub to facilitate data sharing.

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Questions 17

You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying data. Which query type should you use?

Options:

Include ORDER BY DESK on timestamp column and LIMIT to 1.

Use GROUP BY on the unique ID column and timestamp column and SUM on the values.

Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.

Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.

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Questions 18

You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application’s interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.

What should you do?

Options:

Create groups for your users and give those groups access to the dataset

Integrate with a single sign-on (SSO) platform, and pass each user’s credentials along with the queryrequest

Create a service account and grant dataset access to that account. Use the service account’s private key to access the dataset

Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset

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Questions 19

You are designing a Dataflow pipeline for a batch processing job. You want to mitigate multiple zonal failures at job submission time. What should you do?

Options:

Specify a worker region by using the —region flag.

Set the pipeline staging location as a regional Cloud Storage bucket.

Submit duplicate pipelines in two different zones by using the —zone flag.

Create an Eventarc trigger to resubmit the job in case of zonal failure when submitting the job.

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Questions 20

You are developing an Apache Beam pipeline to extract data from a Cloud SQL instance by using JdbclO. You have two projects running in Google Cloud. The pipeline will be deployed and executed on Dataflow in Project A. The Cloud SQL instance is running jn Project B and does not have a public IP address. After deploying the pipeline, you noticed that the pipeline failed to extract data from the Cloud SQL instance due to connection failure. You verified that VPC Service Controls and shared VPC are not in use in these projects. You want to resolve this error while ensuring that the data does not go through the public internet. What should you do?

Options:

Set up VPC Network Peering between Project A and Project B. Add a firewall rule to allow the peered subnet range to access all instances on the network.

Turn off the external IP addresses on the Dataflow worker. Enable Cloud NAT in Project A.

Set up VPC Network Peering between Project A and Project B. Create a Compute Engine instance without external IP address in Project B on the peered subnet to serve as a proxy server to the Cloud SQL database.

Add the external IP addresses of the Dataflow worker as authorized networks in the Cloud SOL instance.

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Questions 21

You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

Options:

Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.

Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. Reference the column TS instead of the column DT from now on.

Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.

Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.

Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.

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Questions 22

Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with Google BigQuery. Which three approaches can you take? (Choose three.)

Options:

Disable writes to certain tables.

Restrict access to tables by role.

Ensure that the data is encrypted at all times.

Restrict BigQuery API access to approved users.

Segregate data across multiple tables or databases.

Use Google Stackdriver Audit Logging to determine policy violations.

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Questions 23

You are using Cloud Bigtable to persist and serve stock market data for each of the major indices. To serve the trading application, you need to access only the most recent stock prices that are streaming in How should you design your row key and tables to ensure that you can access the data with the most simple query?

Options:

Create one unique table for all of the indices, and then use the index and timestamp as the row key design

Create one unique table for all of the indices, and then use a reverse timestamp as the row key design.

For each index, have a separate table and use a timestamp as the row key design

For each index, have a separate table and use a reverse timestamp as the row key design

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Questions 24

You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

Options:

Set the BigQuery dataset to be regional. In the event of an emergency, use a point-in-time snapshot to recover the data.

Set the BigQuery dataset to be regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.

Set the BigQuery dataset to be multi-regional. In the event of an emergency, use a point-in-time snapshot to recover the data.

Set the BigQuery dataset to be multi-regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.

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Questions 25

Your organization stores highly personal data in BigQuery and needs to comply with strict data privacy regulations. You need to ensure that sensitive data values are rendered unreadable whenever an employee leaves the organization. What should you do?

Options:

Use dynamic data masking and revoke viewer permissions when employees leave the organization.

Use column-level access controls with policy tags and revoke viewer permissions when employees leave the organization.

Use AEAD functions and delete keys when employees leave the organization.

Use customer-managed encryption keys (CMEK) and delete keys when employees leave the organization.

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Answer:

Explanation:

Comprehensive and Detailed Explanation:

The core requirement is to make specific data values permanently unreadable, a concept often referred to as cryptographic erasure or "crypto-shredding."

Option C is the correct answer because it achieves this at a granular, field-level. BigQuery's AEAD (Authenticated Encryption with Associated Data) functions allow you to encrypt individual field values within your tables using a keyset from Cloud KMS. You can encrypt the sensitive data, store the encrypted value in BigQuery, and when an employee leaves, you simply destroy the specific KMS key version used for their data. Once the key is destroyed, the encrypted data is permanently unrecoverable, thus rendering it unreadable.

Option A and B are incorrect because data masking and column-level access controls are about controlling access to the data, not about making the underlying data itself unreadable. If an administrator with sufficient privileges were to query the table, the data would still be there in its original form. Revoking permissions prevents a specific user from seeing the data, but it doesn't erase it.

Option D is incorrect because Customer-Managed Encryption Keys (CMEK) operate at the entire table level. Deleting a CMEK key would make the entire table unreadable, which is far too broad and disruptive. The goal is to target specific sensitive values, not destroy whole tables.

Reference (Google Cloud Documentation Concepts):

This solution leverages field-level encryption within BigQuery. The official documentation for "Protecting data with Cloud KMS keys" states that BigQuery's AEAD functions (AEAD.ENCRYPT and AEAD.DECRYPT) are the intended mechanism for this purpose. This approach provides fine-grained control, allowing you to manage the lifecycle of encrypted data by managing the lifecycle of the encryption keys in Cloud Key Management Service (Cloud KMS). The act of destroying a key version is the mechanism for crypto-shredding.

Questions 26

Flowlogistic’s management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

Options:

Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

Cloud Pub/Sub, Cloud Dataflow, and Local SSD

Cloud Pub/Sub, Cloud SQL, and Cloud Storage

Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

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Questions 27

Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO. During testing, you notice that some messages are missing in thedashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?

Options:

Check the dashboard application to see if it is not displaying correctly.

Run a fixed dataset through the Cloud Dataflow pipeline and analyze the output.

Use Google Stackdriver Monitoring on Cloud Pub/Sub to find the missing messages.

Switch Cloud Dataflow to pull messages from Cloud Pub/Sub instead of Cloud Pub/Sub pushing messages to Cloud Dataflow.

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Questions 28

Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

Options:

Create a Google Cloud Dataflow job to process the data.

Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.

Create a Hadoop cluster on Google Compute Engine that uses persistent disks.

Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.

Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.

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Questions 29

You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

Options:

There are very few occurrences of mutations relative to normal samples.

There are roughly equal occurrences of both normal and mutated samples in the database.

You expect future mutations to have different features from the mutated samples in the database.

You expect future mutations to have similar features to the mutated samples in the database.

You already have labels for which samples are mutated and which are normal in the database.

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Questions 30

Your company built a TensorFlow neural-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

Options:

Threading

Serialization

Dropout Methods

Dimensionality Reduction

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Questions 31

Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the data. Which three machine learning applications can you use? (Choose three.)

Options:

Supervised learning to determine which transactions are most likely to be fraudulent.

Unsupervised learning to determine which transactions are most likely to be fraudulent.

Clustering to divide the transactions into N categories based on feature similarity.

Supervised learning to predict the location of a transaction.

Reinforcement learning to predict the location of a transaction.

Unsupervised learning to predict the location of a transaction.

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Questions 32

You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

Options:

Linear regression

Logistic classification

Recurrent neural network

Feedforward neural network

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Questions 33

You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

Options:

Re-write the application to load accumulated data every 2 minutes.

Convert the streaming insert code to batch load for individual messages.

Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.

Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

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Questions 34

You need to modernize your existing on-premises data strategy. Your organization currently uses.

• Apache Hadoop clusters for processing multiple large data sets, including on-premises Hadoop Distributed File System (HDFS) for data replication.

• Apache Airflow to orchestrate hundreds of ETL pipelines with thousands of job steps.

You need to set up a new architecture in Google Cloud that can handle your Hadoop workloads and requires minimal changes to your existing orchestration processes. What should you do?

Options:

Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases Convert your ETL pipelines to Dataflow.

Use Bigtable for your large workloads, with connections to Cloud Storage to handle any HDFS use cases Orchestrate your pipelines with Cloud Composer.

Use Dataproc to migrate your Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Use Cloud Data Fusion to visually design and deploy your ETL pipelines.

Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases.Orchestrate your pipelines with Cloud Composer..

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Answer:

Explanation:

Dataproc is a fully managed service that allows you to run Apache Hadoop and Spark workloads on Google Cloud. It is compatible with the open source ecosystem, so you can migrate your existing Hadoop clusters to Dataproc with minimal changes. Cloud Storage is a scalable, durable, and cost-effective object storage service that can replace HDFS for storing and accessing data. Cloud Storage offers interoperability with Hadoop through connectors, so you can use it as a data source or sink for your Dataproc jobs. Cloud Composer is a fully managed service that allowsyou to create, schedule, and monitor workflows using Apache Airflow. It is integrated with Google Cloud services, such as Dataproc, BigQuery, Dataflow, and Pub/Sub, so you can orchestrate your ETL pipelines across different platforms. Cloud Composer is compatible with your existing Airflow code, so you can migrate your existing orchestration processes to Cloud Composer with minimal changes.

The other options are not as suitable as Dataproc and Cloud Composer for this use case, because they either require more changes to your existing code, or do not meet your requirements. Dataflow is a fully managed service that allows you to create and run scalable data processing pipelines using Apache Beam. However, Dataflow is not compatible with your existing Hadoop code, so you would need to rewrite your ETL pipelines using Beam. Bigtable is a fully managed NoSQL database service that can handle large and complex data sets. However, Bigtable is not compatible with your existing Hadoop code, so you would need to rewrite your queries and applications using Bigtable APIs. Cloud Data Fusion is a fully managed service that allows you to visually design and deploy data integration pipelines using a graphical interface. However, Cloud Data Fusion is not compatible with your existing Airflow code, so you would need to recreate your orchestration processes using Cloud Data Fusion UI. References:

Dataproc overview

Cloud Storage connector for Hadoop

Cloud Composer overview

Questions 35

You stream order data by using a Dataflow pipeline, and write the aggregated result to Memorystore. You provisioned a Memorystore for Redis instance with Basic Tier. 4 GB capacity, which is used by 40 clients for read-only access. You are expecting the number of read-only clients to increase significantly to a few hundred and you need to be able to support the demand. You want to ensure that read and write access availability is not impacted, and any changes you make can be deployed quickly. What should you do?

Options:

Create multiple new Memorystore for Redis instances with Basic Tier (4 GB capacity) Modify the Dataflow pipeline and new clients to use all instances

Create a new Memorystore for Redis instance with Standard Tier Set capacity to 4 GB and read replica to No read replicas (high availability only). Delete the old instance.

Create a new Memorystore for Memcached instance Set a minimum of three nodes, and memory per node to 4 GB. Modify the Dataflow pipeline and all clients to use the Memcached instance Delete the old instance.

Create a new Memorystore for Redis instance with Standard Tier Set capacity to 5 GB and create multiple read replicas Delete the old instance.

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Answer:

Explanation:

The Basic Tier of Memorystore for Redis provides a standalone Redis instance that is not replicated and does not support read replicas. This means that it cannot scale horizontally to handle more read requests, and it does not provide high availability or automatic failover. If thenumber of read-only clients increases significantly, the Basic Tier instance may not be able to handle the demand and may impact the read and write access availability. Therefore, option A is not a good solution, as it would require creating multiple Basic Tier instances and modifying the Dataflow pipeline and the clients to distribute the load among them. This would increase the complexity and the management overhead of the solution.

The Standard Tier of Memorystore for Redis provides a highly available Redis instance that supports replication and read replicas. Replication ensures that the data is backed up in another zone and can fail over automatically in case of a primary node failure. Read replicas allow scaling the read throughput by adding up to five replicas to an instance and using them for read-only queries. The Standard Tier also supports in-transit encryption and maintenance windows. Therefore, option D is the best solution, as it would create a new Standard Tier instance with a higher capacity (5 GB) and multiple read replicas to handle the increased demand. The old instance can be deleted after migrating the data to the new instance.

Option B is not a good solution, as it would create a new Standard Tier instance with the same capacity (4 GB) and no read replicas. This would not improve the read throughput or the availability of the solution. Option C is not a good solution, as it would create a new Memorystore for Memcached instance, which is a different service that uses a different protocol and data model than Redis. This would require changing the code of the Dataflow pipeline and the clients to use the Memcached protocol and data structures, which would take more time and effort than migrating to a new Redis instance. References: Redis tier capabilities | Memorystore for Redis | Google Cloud, Pricing | Memorystore for Redis | Google Cloud, What is Memorystore? | Google Cloud Blog, Working with GCP Memorystore - Simple Talk - Redgate Software

Questions 36

You are implementing security best practices on your data pipeline. Currently, you are manually executing jobs as the Project Owner. You want to automate these jobs by taking nightly batch files containing non-public information from Google Cloud Storage, processing them with a Spark Scala job on a Google Cloud Dataproc cluster, and depositing the results into Google BigQuery.

How should you securely run this workload?

Options:

Restrict the Google Cloud Storage bucket so only you can see the files

Grant the Project Owner role to a service account, and run the job with it

Use a service account with the ability to read the batch files and to write to BigQuery

Use a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files and write to BigQuery

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Questions 37

Your financial services company is moving to cloud technology and wants to store 50 TB of financial timeseries data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.

Which product should they use to store the data?

Options:

Cloud Bigtable

Google BigQuery

Google Cloud Storage

Google Cloud Datastore

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Questions 38

Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.

Which approach should you take?

Options:

Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.

Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.

Use the NOW () function in BigQuery to record the event’s time.

Use the automatically generated timestamp from Cloud Pub/Sub to order the data.

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Questions 39

You want to schedule a number of sequential load and transformation jobs Data files will be added to a Cloud Storage bucket by an upstream process There is no fixed schedule for when the new data arrives Next, a Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to run additional transformation jobs in BigQuery The transformation jobs are different for every table These jobs might take hours to complete You need to determine the most efficient and maintainable workflow to process hundreds of tables and provide the freshest data to your end users. What should you do?

Options:

1Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage. Dataproc. and BigQuery operators2 Use a single shared DAG for all tables that need to go through the pipeline3 Schedule the DAG to run hourly

1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.2 Create a separate DAG for each table that needs to go through the pipeline3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc. and BigQuery operators2 Create a separate DAG for each table that needs to go through the pipeline3 Schedule the DAGs to run hourly

1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators2 Use a single shared DAG for all tables that need to go through the pipeline.3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

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Questions 40

You are designing a data warehouse in BigQuery to analyze sales data for a telecommunication service provider. You need to create a data model for customers, products, and subscriptions All customers, products, and subscriptions can be updated monthly, but you must maintain a historical record of all data. You plan to use the visualization layer for current and historical reporting. You need to ensure that the data model is simple, easy-to-use. and cost-effective. What should you do?

Options:

Create a normalized model with tables for each entity. Use snapshots before updates to track historical data

Create a normalized model with tables for each entity. Keep all input files in a Cloud Storage bucket to track historical data

Create a denormalized model with nested and repeated fields Update the table and use snapshots to track historical data

Create a denormalized, append-only model with nested and repeated fields Use the ingestion timestamp to track historical data.

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Questions 41

Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?

Options:

Store the common data in BigQuery as partitioned tables.

Store the common data in BigQuery and expose authorized views.

Store the common data encoded as Avro in Google Cloud Storage.

Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.

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Questions 42

MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

Options:

Rowkey: date#device_idColumn data: data_point

Rowkey: dateColumn data: device_id, data_point

Rowkey: device_idColumn data: date, data_point

Rowkey: data_pointColumn data: device_id, date

Rowkey: date#data_pointColumn data: device_id

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Questions 43

Flowlogistic’s CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they’ve purchased a visualization tool to simplify the creation of BigQuery reports. However, they’ve been overwhelmed by all thedata in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

Options:

Export the data into a Google Sheet for virtualization.

Create an additional table with only the necessary columns.

Create a view on the table to present to the virtualization tool.

Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

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Questions 44

An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted. How should you build this pipeline?

Options:

Use federated data sources, and check data in the SQL query.

Enable BigQuery monitoring in Google Stackdriver and create an alert.

Import the data into BigQuery using the gcloud CLI and set max_bad_records to 0.

Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors to another dead-letter table for analysis.

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Questions 45

You are building a model to make clothing recommendations. You know a user’s fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?

Options:

Continuously retrain the model on just the new data.

Continuously retrain the model on a combination of existing data and the new data.

Train on the existing data while using the new data as your test set.

Train on the new data while using the existing data as your test set.

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Questions 46

You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:

The user profile: What the user likes and doesn’t like to eat

The user account information: Name, address, preferred meal times

The order information: When orders are made, from where, to whom

The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?

Options:

BigQuery

Cloud SQL

Cloud Bigtable

Cloud Datastore

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Questions 47

MJTelco’s Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

Options:

The zone

The number of workers

The disk size per worker

The maximum number of workers

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Questions 48

Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?

Options:

Rewrite the job in Pig.

Rewrite the job in Apache Spark.

Increase the size of the Hadoop cluster.

Decrease the size of the Hadoop cluster but also rewrite the job in Hive.

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Questions 49

You need to compose visualization for operations teams with the following requirements:

Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)

The report must not be more than 3 hours delayed from live data.

The actionable report should only show suboptimal links.

Most suboptimal links should be sorted to the top.

Suboptimal links can be grouped and filtered by regional geography.

User response time to load the report must be <5 seconds.

You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?

Options:

Look through the current data and compose a series of charts and tables, one for each possiblecombination of criteria.

Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.

Export the data to a spreadsheet, compose a series of charts and tables, one for each possiblecombination of criteria, and spread them across multiple tabs.

Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.

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Questions 50

You need to compose visualizations for operations teams with the following requirements:

Which approach meets the requirements?

Options:

Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.

Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.

Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.

Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.

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Questions 51

You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.

Which two actions should you take? (Choose two.)

Options:

Ensure all the tables are included in global dataset.

Ensure each table is included in a dataset for a region.

Adjust the settings for each table to allow a related region-based security group view access.

Adjust the settings for each view to allow a related region-based security group view access.

Adjust the settings for each dataset to allow a related region-based security group view access.

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Questions 52

To give a user read permission for only the first three columns of a table, which access control method would you use?

Options:

Primitive role

Predefined role

Authorized view

It's not possible to give access to only the first three columns of a table.

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Questions 53

Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day’s events. They also want to use streaming ingestion. What should you do?

Options:

Create a table called tracking_table and include a DATE column.

Create a partitioned table called tracking_table and include a TIMESTAMP column.

Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.

Create a table called tracking_table with a TIMESTAMP column to represent the day.

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Questions 54

Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.

Options:

Blaze

Spark

Fire

Ignite

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Questions 55

You want to use a BigQuery table as a data sink. In which writing mode(s) can you use BigQuery as a sink?

Options:

Both batch and streaming

BigQuery cannot be used as a sink

Only batch

Only streaming

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Questions 56

For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

Options:

Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.

Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.

Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.

Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.

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Questions 57

MJTelco is building a custom interface to share data. They have these requirements:

They need to do aggregations over their petabyte-scale datasets.

They need to scan specific time range rows with a very fast response time (milliseconds).

Which combination of Google Cloud Platform products should you recommend?

Options:

Cloud Datastore and Cloud Bigtable

Cloud Bigtable and Cloud SQL

BigQuery and Cloud Bigtable

BigQuery and Cloud Storage

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Questions 58

Cloud Bigtable is a recommended option for storing very large amounts of ____________________________?

Options:

multi-keyed data with very high latency

multi-keyed data with very low latency

single-keyed data with very low latency

single-keyed data with very high latency

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Questions 59

Which of these numbers are adjusted by a neural network as it learns from a training dataset (select 2 answers)?

Options:

Weights

Biases

Continuous features

Input values

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Questions 60

Cloud Dataproc charges you only for what you really use with _____ billing.

Options:

month-by-month

minute-by-minute

week-by-week

hour-by-hour

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Questions 61

Which of these is NOT a way to customize the software on Dataproc cluster instances?

Options:

Set initialization actions

Modify configuration files using cluster properties

Configure the cluster using Cloud Deployment Manager

Log into the master node and make changes from there

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Questions 62

Which Java SDK class can you use to run your Dataflow programs locally?

Options:

LocalRunner

DirectPipelineRunner

MachineRunner

LocalPipelineRunner

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Questions 63

Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

Options:

An hourly watermark

An event time trigger

The with Allowed Lateness method

A processing time trigger

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Questions 64

Does Dataflow process batch data pipelines or streaming data pipelines?

Options:

Only Batch Data Pipelines

Both Batch and Streaming Data Pipelines

Only Streaming Data Pipelines

None of the above

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Questions 65

You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?

Options:

Change the processing job to use Google Cloud Dataproc instead.

Manually start the Cloud Dataflow job each morning when you get into the office.

Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.

Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.

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Questions 66

Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

Options:

The CSV data loaded in BigQuery is not flagged as CSV.

The CSV data has invalid rows that were skipped on import.

The CSV data loaded in BigQuery is not using BigQuery’s default encoding.

The CSV data has not gone through an ETL phase before loading into BigQuery.

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Questions 67

You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.

You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)

Options:

Redis

HBase

MySQL

MongoDB

Cassandra

HDFS with Hive

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Questions 68

Which of the following is not true about Dataflow pipelines?

Options:

Pipelines are a set of operations

Pipelines represent a data processing job

Pipelines represent a directed graph of steps

Pipelines can share data between instances

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Questions 69

Which software libraries are supported by Cloud Machine Learning Engine?

Options:

Theano and TensorFlow

Theano and Torch

TensorFlow

TensorFlow and Torch

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Questions 70

Which of these operations can you perform from the BigQuery Web UI?

Options:

Upload a file in SQL format.

Load data with nested and repeated fields.

Upload a 20 MB file.

Upload multiple files using a wildcard.

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Questions 71

Which of the following is NOT true about Dataflow pipelines?

Options:

Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner

Dataflow pipelines can consume data from other Google Cloud services

Dataflow pipelines can be programmed in Java

Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

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Questions 72

Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

Options:

Hive

Pig

YARN

Spark

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Questions 73

When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.

Options:

zone

node

label

type

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Questions 74

Which of these is not a supported method of putting data into a partitioned table?

Options:

If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.

Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".

Create a partitioned table and stream new records to it every day.

Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".

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Questions 75

You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity ‘Movie’ the property ‘actors’ and the property ‘tags’ have multiple values but the property ‘date released’ does not. A typical query would ask for all movies with actor= ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

Options:

Option A

Option B.

Option C

Option D

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Questions 76

You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

Options:

Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.

Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.

Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery.

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Questions 77

Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.

You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

Options:

Introduce data compression for each file to increase the rate file of file transfer.

Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.

Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.

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Questions 78

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

Options:

Load the data every 30 minutes into a new partitioned table in BigQuery.

Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

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Questions 79

What are two of the characteristics of using online prediction rather than batch prediction?

Options:

It is optimized to handle a high volume of data instances in a job and to run more complex models.

Predictions are returned in the response message.

Predictions are written to output files in a Cloud Storage location that you specify.

It is optimized to minimize the latency of serving predictions.

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Questions 80

You have a job that you want to cancel. It is a streaming pipeline, and you want to ensure that any data that is in-flight is processed and written to the output. Which of the following commands can you use on the Dataflow monitoring console to stop the pipeline job?

Options:

Cancel

Drain

Stop

Finish

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Exam Code: Professional-Data-Engineer

Exam Name: Google Professional Data Engineer Exam

Last Update: Nov 18, 2025

Questions: 387

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Professional-Data-Engineer Google Professional Data Engineer Exam Questions and Answers

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