Databricks-Certified-Data-Analyst-Associate Databricks Certified Data Analyst Associate Exam Questions and Answers

Databricks provides a user-friendly interface that allows data analysts to quickly upload small datasets, such as a 50-row CSV file, and create tables within Databricks SQL. The steps are as follows:​

Access the Data Upload Interface:

In the Databricks workspace, navigate to the sidebar and click on New > Add or upload data.​

Select Create or modify a table.​

Upload the CSV File:

Click on the browse button or drag and drop the CSV file directly onto the designated area.​

The interface supports uploading up to 10 files simultaneously, with a total size limit of 2 GB.​

Configure Table Settings:

After uploading, a preview of the data is displayed.​

Specify the table name, select the appropriate schema, and configure any additional settings as needed.​

Create the Table:

Once all configurations are set, click on the Create Table button to finalize the process.​

This method is efficient for quickly importing small datasets without the need for additional tools or complex configurations. Options B, C, and D involve more complex or manual processes that are unnecessary for this task.​

Option B is correct. A 20 MB CSV file with about 50,000 records is small enough for a local pandas workflow. A real-time streaming solution, a distributed Spark cluster, or Hadoop MapReduce would add unnecessary complexity for a small single-file batch analysis. Databricks documentation explains that pandas is available in Databricks Runtime and can be used for data analysis, while pandas API on Spark is useful when pandas-style workloads need to scale beyond smaller datasets. Databricks also notes that pandas does not scale out to big data, which is exactly why Spark or pandas API on Spark is used for larger workloads, not for a small 20 MB file.

 Option A uses the SELECT DISTINCT statement to remove duplicate rows from the table_bronze and create a new table table_silver with the deduplicated data. This is the correct way to deduplicate data using Spark SQL12. Option B simply inserts all the rows from table_bronze into table_silver, without removing any duplicates. Option C is not a valid syntax for Spark SQL, as there is no MERGE DEDUPLICATE statement. Option D appends all the rows from table_bronze into table_silver, without removing any duplicates. Option E overwrites the existing data in table_silver with the data from table_bronze, without removing any duplicates. References: Delete Duplicate using SPARK SQL, Spark SQL - How to Remove Duplicate Rows

In this scenario, the data engineering team has configured a Structured Streaming pipeline that updates the gold-level tables every 10 minutes. To ensure that the dashboard reflects the most recent data, it is essential to set the dashboard ' s refresh schedule to an interval of 10 minutes or less. This synchronization ensures that stakeholders view the latest information shortly after it becomes available in the gold-level tables. Options B, C, and D do not directly address the requirement of aligning the dashboard refresh frequency with the data update interval.

Variance is a statistical measure that quantifies the dispersion or spread of a set of values around their mean (central value). It is calculated by taking the average of the squared differences between each value and the mean of the dataset. A higher variance indicates that the data points are more spread out from the mean, while a lower variance suggests that they are closer to the mean. This measure is fundamental in statistics to understand the degree of variability within a dataset.​WikipediaWikipedia+1Investopedia+1

The correct answer is C because Databricks SQL supports bracket notation for arrays, and array indexing starts at 0. Therefore, the first element of the products array is accessed with products[0]. Options A and B use dot notation, which is not the correct syntax for array indexing. Option D returns the second element, not the first.

Official documentation extract used: Databricks states that the bracket operator returns an array element by index and that “the first element of an ARRAY is at index 0.”

In Databricks, to determine the owner of a managed table, you can utilize the Catalog Explorer feature. The steps are as follows:​

Access Catalog Explorer:

In your Databricks workspace, click on the Catalog icon in the sidebar to open Catalog Explorer.​

Navigate to the Table:

Within Catalog Explorer, browse through the catalog and schema to locate the specific managed table whose ownership you wish to verify.​

View Table Details:

Click on the table name to open its details page.​

Identify the Owner:

On the table ' s details page, review the Owner field, which displays the principal (user, service principal, or group) that owns the table.​

This method provides a straightforward way to ascertain the ownership of managed tables within the Databricks environment. Understanding table ownership is essential for managing permissions and ensuring proper access control.​

 Databricks SQL uses a query cache to store the results of queries that have been executed previously. This improves the performance and efficiency of repeated queries. To determine if a query result was pulled from the cache, you can go to the Query History tab in the Databricks SQL UI and click on the text of the query. A slideout will appear on the right side of the screen, showing the query details, including the cache status. If the result came from the cache, the cache status will show “Cached”. If the result did not come from the cache, the cache status will show “Not cached”. You can also see the cache hit ratio, which is the percentage of queries that were served from the cache. References: The answer can be verified from Databricks SQL documentation which provides information on how to use the query cache and how to check the cache status. Reference link: Databricks SQL - Query Cache

Option C is correct. In Data Vault modeling, hubs represent core business entities, satellites store descriptive and historical attributes, and links represent relationships between hubs. A many-to-many relationship between business entities is therefore captured by a Link Table. The Databricks Data Analyst Associate Exam Guide includes Data Vault schemas as part of data modeling with Databricks SQL, and Databricks’ Data Vault guidance explains that links represent relationships between hub entities. References: Databricks Data Analyst Associate Exam Guide and Databricks Data Vault guidance.

A Sankey diagram is a type of visualization that shows the flow of data between different nodes or categories. It is often used to represent the movement of users through a website, as it can show the paths they take, the sources they come from, the pages they visit, and the outcomes they achieve. A Sankey diagram consists of links and nodes, where the links represent the volume or weight of the flow, and the nodes represent the stages or steps of the flow. The width of the links is proportional to the amount of flow, and the color of the links can indicate different attributes or segments of the flow. A Sankey diagram can help identify the most common or popular user journeys, the bottlenecks or drop-offs in the flow, and the opportunities for improvement or optimization. References: The answer can be verified from Databricks documentation which provides examples and instructions on how to create Sankey diagrams using Databricks SQL Analytics and Databricks Visualizations. Reference links: Databricks SQL Analytics - Sankey Diagram, Databricks Visualizations - Sankey Diagram

To get the number of orders per customer, you need to join the customers and orders tables on the customer_id, count the order_id, and group the results by customer_name. The correct SQL syntax, as outlined in Databricks SQL documentation, is to use GROUP BY on the selected customer field and use COUNT for aggregation. Only option A does this correctly, while the other options contain syntax errors or incorrect field names.

Delta Lake is a storage layer that enhances data lakes with features like ACID transactions, schema enforcement, and time travel. While it stores table data as Parquet files, Delta Lake also keeps a transaction log (stored in the _delta_log directory) that contains detailed table metadata.

This metadata includes:

Table schema

Partitioning information

Data file paths

Transactional operations like inserts, updates, and deletes

Commit history and version control

This metadata is critical for supporting Delta Lake’s advanced capabilities such as time travel and efficient query execution. Delta Lake does not store data summary visualizations or owner account information directly alongside the data files.

A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard. References: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL

Option D is correct. Delta Live Tables / Lakeflow Declarative Pipelines supports expectations, which are rules used to validate data as it flows through ETL pipelines. These expectations provide automated visibility into data quality and can be configured to fail updates or drop invalid records. Unity Catalog governs data, Data Explorer/Catalog Explorer helps browse data, Delta Lake provides reliable table storage, and Auto Loader ingests files incrementally, but the pipeline-level automated data quality monitoring tool is Delta Live Tables expectations. Official Databricks extract: expectations “validate data as it flows through ETL pipelines” and provide “data quality metrics.”

The DROP TABLE command removes a table from the metastore and deletes the associated data files. The syntax for this command is DROP TABLE [IF EXISTS] [database_name.] table_name;. The optional IF EXISTS clause prevents an error if the table does not exist. The optional database_name. prefix specifies the database where the table resides. If not specified, the current database is used. Therefore, the correct command to remove the table table_name from the database database_name and all of the data files associated with it is DROP TABLE database_name.table_name;. The other commands are either invalid syntax or would produce undesired results. References: Databricks - DROP TABLE

Option E is correct. In the current Databricks UI, Data Explorer is known as Catalog Explorer. It is the location used to browse catalogs, schemas, tables, and views, review object metadata, and request or inspect access-related information. DBFS, Jobs, Dashboards, and Repos are not the table-permission review location. Official Databricks extract: users can navigate the catalog tree, open an object’s page, and request additional privileges from Catalog Explorer.

Option C is correct because Auto Stop is specifically designed to stop a SQL warehouse when it has been idle for a configured number of minutes. This minimizes total warehouse running time after the scheduled dashboard refresh completes. Reducing cluster size may reduce hourly cost but does not stop the warehouse. Serverless can help with management, but the feature directly matching “only running when necessary” is Auto Stop. Official Databricks extract: “Auto Stop determines whether the warehouse stops if it’s idle for the specified number of minutes.” Databricks also notes that idle SQL warehouses continue to accumulate charges until stopped.

Databricks SQL allows you to create multiple visualizations from a single query result. These visualizations can be customized independently and each can be added to a dashboard. This feature is explicitly supported and recommended in Databricks’ documentation on dashboards and visualization workflows, enabling flexible reporting without duplicating queries.

Option C is correct. DESCRIBE DATABASE customer360 returns metadata for the database/schema, including its filesystem location. DESCRIBE LOCATION is for external locations, not databases. DROP DATABASE deletes a database, ALTER DATABASE changes metadata/properties, and USE DATABASE changes the current database context. Official Databricks extract: DESCRIBE DATABASE is an alias for DESCRIBE SCHEMA, and DESCRIBE SCHEMA returns metadata including the schema’s “location on the filesystem.”

Option E is correct. A Silver-to-Gold hop typically reads cleaned/refined Silver data and writes aggregated, analytics-ready Gold data. The query reads from sales, groups by store, and aggregates sum( " sales " ), producing a summary table suitable for reporting or dashboarding. That matches the Gold layer. Option A reads from a raw location, which is not Silver-to-Gold. Option D filters invalid units, which is a cleaning step associated with Silver. Options B and C add a derived column but do not create a Gold-level aggregated table. Official Databricks medallion architecture documentation states that Silver is where data cleanup and validation are performed, while the Gold layer “consists of aggregated data tailored for analytics and reporting.” Databricks Structured Streaming documentation also shows .writeStream.outputMode( " complete " ).toTable(...) as a valid output mode pattern for stateful streaming aggregations.

Option C is correct because the requirement is to run the SQL query and then operate on the results in PySpark. spark.sql(...) executes SQL text and returns a Spark DataFrame that can be used in PySpark tests. spark.table(...) can load a table directly, but it does not run an arbitrary SQL query. Option A is SQL text by itself, not a PySpark operation. Official Databricks documentation states that a SparkSession can be used to execute SQL over tables, and Databricks notebook documentation specifically references using SQL inside Python with a spark.sql command.

The correct query to get the percentage rank of products within region by the sales is option B. This query uses the PERCENT_RANK() window function to calculate the relative rank of each product within each region based on the sales amount. The window function is partitioned by region and ordered by sales in descending order. The result is aliased as rank and displayed along with the region and product columns. The other options are incorrect because:

A. Option A uses the RANK() window function instead of the PERCENT_RANK() function. The RANK() function returns the rank of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM().

C. Option C uses the DENSE_RANK() window function instead of the PERCENT_RANK() function. The DENSE_RANK() function returns the rank of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM().

D. Option D uses the ROW_NUMBER() window function instead of the PERCENT_RANK() function. The ROW_NUMBER() function returns the sequential number of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM(). References:

1: PERCENT_RANK (Transact-SQL)

2: Window functions in Databricks SQL

3: Databricks Certified Data Analyst Associate Exam Guide

According to the Databricks SQL documentation, when a data analyst clicks on the “Add visualization” button on the SQL Editor page, the default visualization type is Bar Chart. This is because the result set has two columns: one of type STRING and one of type INT. The Bar Chart visualization automatically assigns the STRING column to the X-axis and the INT column to the Y-axis. The Bar Chart visualization is suitable for showing the distribution of a numeric variable across different categories. References: Visualization in Databricks SQL, Visualization types

Option D is correct. A temporary view is session-scoped and does not store physical data. It is suitable when the relational object is only needed in the current session or query context and should not be available to others in other sessions. A regular view also avoids copying physical data, but it is persistent and can be shared beyond the current session. Official Databricks extract: a view is a “virtual table that has no physical data,” and temporary views are scoped to the notebook/script or query level and cannot be referenced outside that scope.

The correct answer is A because MERGE INTO is the Databricks SQL command used to combine source rows with a target table based on a match condition. The condition checks whether supplier_id already exists. If a row does not match, WHEN NOT MATCHED THEN INSERT * inserts it. If a row already matches, no WHEN MATCHED action is provided, so the existing row remains unchanged. This prevents duplicate supplier rows.

Official documentation extract used: Databricks shows the pattern MERGE INTO target USING source ON target.key = source.key WHEN NOT MATCHED THEN INSERT *.

Option B is correct. A data analyst commonly uses Databricks SQL for analytics-focused transformations, aggregation, joining, filtering, and preparation of datasets for reporting or dashboarding. Analysis-specific ETL on Gold-layer tables is a “last-mile” analytics task: the Gold layer is already curated for business use, and the analyst performs additional project-specific refinement. The official Databricks exam guide includes creating views, performing aggregate operations, combining tables with joins, filtering, sorting, and using dashboards as Data Analyst Associate skills. It also states that Gold-layer data drives downstream analytics and dashboards. Options A, C, and D are primarily streaming engineering, machine learning, and MLOps activities, not common Databricks SQL analytics applications. References: Databricks Certified Data Analyst Associate Exam Guide and medallion architecture documentation.

Option C is correct. If an analyst encounters PII that they should not access, they should stop using that data and escalate through the appropriate organizational process. They should not delete the table, drop the column, or silently continue. Databricks security and privacy guidance emphasizes least-privilege access, data classification by sensitivity, access control, and meeting compliance/privacy requirements, including redaction or obfuscation of PII when required. Therefore, notifying a supervisor or data owner is the correct professional and governance-aligned response. References: Databricks security, compliance, and privacy documentation.

Data analysts should consider all of these factors when working with PII data, as they may affect the data security, privacy, compliance, and quality. PII data is any information that can be used to identify a specific individual, such as name, address, phone number, email, social security number, etc. PII data may be subject to different legal and ethical obligations depending on the context and location of the data collection and analysis. For example, some countries or regions may have stricter data protection laws than others, such as the General Data Protection Regulation (GDPR) in the European Union. Data analysts should also follow the organization-specific best practices for PII data, such as encryption, anonymization, masking, access control, auditing, etc. These best practices can help prevent data breaches, unauthorized access, misuse, or loss of PII data. References:

How to Use Databricks to Encrypt and Protect PII Data

Automating Sensitive Data (PII/PHI) Detection

Databricks Certified Data Analyst Associate

The explode function is used to transform a DataFrame column of arrays or maps into multiple rows, duplicating the other column’s values. In this context, it will be used to expand the nested array column products in the transactions table so that each unique item in products for each row has its own row and the transaction_id column is duplicated as necessary. References: Databricks Documentation

I also noticed that you sent me an image along with your message. The image shows a snippet of SQL code that is incomplete. It begins with “SELECT” indicating a query to retrieve data. “transaction_id,” suggests that transaction_id is one of the columns being selected. There are blanks indicated by underscores where certain parts of the SQL command should be, including what appears to be an alias for a column and part of the FROM clause. The query ends with “FROM transactions;” indicating data is being selected from a ‘transactions’ table.

If you are interested in learning more about Databricks Data Analyst Associate certification, you can check out the following resources:

Databricks Certified Data Analyst Associate: This is the official page for the certification exam, where you can find the exam guide, registration details, and preparation tips.

Data Analysis With Databricks SQL: This is a self-paced course that covers the topics and skills required for the certification exam. You can access it for free on Databricks Academy.

Tips for the Databricks Certified Data Analyst Associate Certification: This is a blog post that provides some useful advice and study tips for passing the certification exam.

Databricks Certified Data Analyst Associate Certification: This is another blog post that gives an overview of the certification exam and its benefits.

Option C is the correct answer because it creates a new aggregated table, store_sales, from the existing cleaned/refined table cleaned_transactions. In a medallion architecture, a table such as cleaned_transactions represents a Silver-layer table because it contains cleaned or validated transaction-level data. The query then creates store_sales by grouping by store_id and calculating SUM(sales), which is an aggregation suitable for analytics and reporting. That is exactly the Silver-to-Gold hop.

Option A creates cleaned_transactions from transactions, so it is moving toward a cleaned Silver table, not creating a Gold table. Option B creates transactions from raw_transactions, which is closer to a raw-to-Bronze or Bronze preparation step. Option D filters rows from transactions into cleaned_transactions, which is also a cleaning/validation step associated with Silver, not Gold.

Exact extract from official Databricks documentation: Databricks describes medallion architecture as improving data through “Bronze ⇒ Silver ⇒ Gold layer tables.” The same official page identifies Silver as “Data cleaning and validation” and Gold as “Dimensional modeling and aggregation.” Databricks also states that the Gold layer “consists of aggregated data tailored for analytics and reporting.”

Option B is correct. The problem is many users running small queries simultaneously, which is a concurrency issue. Increasing the maximum number of clusters lets the SQL warehouse scale out to serve more concurrent queries. Increasing cluster size is more useful for complex or resource-heavy queries, not necessarily many small simultaneous queries. Auto Stop reduces cost but does not improve active-query latency. Official Databricks extract: “You can increase the maximum clusters if you want to handle more concurrent users,” and Databricks recommends monitoring queued queries and adjusting maximum clusters.