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

Questions 4

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:

A.

Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

B.

Cloud Pub/Sub, Cloud Dataflow, and Local SSD

C.

Cloud Pub/Sub, Cloud SQL, and Cloud Storage

D.

Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

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Questions 5

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:

A.

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

B.

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

C.

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

D.

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

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Questions 6

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:

A.

Store the common data in BigQuery as partitioned tables.

B.

Store the common data in BigQuery and expose authorized views.

C.

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

D.

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

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Questions 7

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 the data 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:

A.

Export the data into a Google Sheet for virtualization.

B.

Create an additional table with only the necessary columns.

C.

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

D.

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

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Questions 8

Why do you need to split a machine learning dataset into training data and test data?

Options:

A.

So you can try two different sets of features

B.

To make sure your model is generalized for more than just the training data

C.

To allow you to create unit tests in your code

D.

So you can use one dataset for a wide model and one for a deep model

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Questions 9

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

Options:

A.

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.

B.

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".

C.

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

D.

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 10

When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

Options:

A.

500 TB

B.

1 GB

C.

1 TB

D.

500 GB

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Questions 11

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

Options:

A.

Hive

B.

Pig

C.

YARN

D.

Spark

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Questions 12

Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?

Options:

A.

dataflow.worker

B.

dataflow.compute

C.

dataflow.developer

D.

dataflow.viewer

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Questions 13

If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

Options:

A.

Do not use a production instance.

B.

Run your test for at least 10 minutes.

C.

Before you test, run a heavy pre-test for several minutes.

D.

Use at least 300 GB of data.

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Questions 14

You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

Options:

A.

Create a cron schedule in Cloud Dataprep.

B.

Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.

C.

Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.

D.

Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.

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Questions 15

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:

A.

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

B.

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

C.

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

D.

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 16

You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

Options:

A.

Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.

B.

Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

C.

Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.

D.

Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

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Questions 17

You are migrating an application that tracks library books and information about each book, such as author or year published, from an on-premises data warehouse to BigQuery In your current relational database, the author information is kept in a separate table and joined to the book information on a common key Based on Google's recommended practice for schema design, how would you structure the data to ensure optimal speed of queries about the author of each book that has been borrowed?

Options:

A.

Keep the schema the same, maintain the different tables for the book and each of the attributes, and query as you are doing today

B.

Create a table that is wide and includes a column for each attribute, including the author's first name, last name, date of birth, etc

C.

Create a table that includes information about the books and authors, but nest the author fields inside the author column

D.

Keep the schema the same, create a view that joins all of the tables, and always query the view

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Questions 18

Your company is implementing a data warehouse using BigQuery, and you have been tasked with designing the data model You move your on-premises sales data warehouse with a star data schema to BigQuery but notice performance issues when querying the data of the past 30 days Based on Google's recommended practices, what should you do to speed up the query without increasing storage costs?

Options:

A.

Denormalize the data

B.

Shard the data by customer ID

C.

Materialize the dimensional data in views

D.

Partition the data by transaction date

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Questions 19

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:

A.

Ensure all the tables are included in global dataset.

B.

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

C.

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

D.

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

E.

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

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Questions 20

Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub

streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

Options:

A.

They have not assigned the timestamp, which causes the job to fail

B.

They have not set the triggers to accommodate the data coming in late, which causes the job to fail

C.

They have not applied a global windowing function, which causes the job to fail when the pipeline is

created

D.

They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created

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Questions 21

You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

Options:

A.

Use Cloud SQL for storage. Add secondary indexes to support query patterns.

B.

Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.

C.

Use Cloud Spanner for storage. Add secondary indexes to support query patterns.

D.

Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.

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Questions 22

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:

A.

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

B.

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

C.

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

D.

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

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Questions 23

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:

A.

Specify the TableReference object in the code.

B.

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

C.

Use of both the Google BigQuery TableSchema and TableFieldSchema classes.

D.

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 24

Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

Options:

A.

Issue a command to restart the database servers.

B.

Retry the query with exponential backoff, up to a cap of 15 minutes.

C.

Retry the query every second until it comes back online to minimize staleness of data.

D.

Reduce the query frequency to once every hour until the database comes back online.

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Questions 25

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:

A.

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

B.

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

C.

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

D.

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

E.

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

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Questions 26

Your startup has never implemented a formal security policy. Currently, everyone in the company has access to the datasets stored in Google BigQuery. Teams have freedom to use the service as they see fit, and they have not documented their use cases. You have been asked to secure the data warehouse. You need to discover what everyone is doing. What should you do first?

Options:

A.

Use Google Stackdriver Audit Logs to review data access.

B.

Get the identity and access management IIAM) policy of each table

C.

Use Stackdriver Monitoring to see the usage of BigQuery query slots.

D.

Use the Google Cloud Billing API to see what account the warehouse is being billed to.

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Questions 27

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:

A.

Load data into different partitions.

B.

Load data into a different dataset for each client.

C.

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

D.

Restrict a client’s dataset to approved users.

E.

Only allow a service account to access the datasets.

F.

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

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Questions 28

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:

A.

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

B.

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

C.

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

D.

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

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Questions 29

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:

A.

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

B.

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

C.

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

D.

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.

E.

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 30

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:

A.

Option A

B.

Option B.

C.

Option C

D.

Option D

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Questions 31

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:

A.

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

B.

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

C.

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

D.

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 32

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:

A.

Change the processing job to use Google Cloud Dataproc instead.

B.

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

C.

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

D.

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

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Questions 33

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:

A.

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

B.

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.

C.

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.

D.

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 34

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:

A.

Rewrite the job in Pig.

B.

Rewrite the job in Apache Spark.

C.

Increase the size of the Hadoop cluster.

D.

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

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Questions 35

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:

A.

Redis

B.

HBase

C.

MySQL

D.

MongoDB

E.

Cassandra

F.

HDFS with Hive

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Exam Name: Google Professional Data Engineer Exam
Last Update: Jun 9, 2023
Questions: 268
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