Databricks Certified Data Analyst Associate
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The Databricks Certified Data Analyst Associate certification exam assesses an individual's ability to use the Databricks SQL service to complete introductory data analysis tasks. This includes an understanding of the Databricks SQL service and its capabilities, an ability to manage data with Databricks tools following best practices, using SQL to complete data tasks in the Lakehouse, creating production-grade data visualizations and dashboards, and developing analytics applications to solve common data analytics problems. Individuals who pass this certification exam can be expected to complete basic data analysis tasks using Databricks SQL and its associated capabilities.
Exam Details
Type: Proctored certification
Total number of questions: 45
Time limit: 90 minutes
Registration fee: $200
Question types: Multiple choice
Test aides: None allowed
Languages: English
Delivery method: Online proctored
Prerequisites: None, but related training highly recommended
Recommended experience: 6+ months of hands-on experience performing the data analysis tasks outlined in the exam guide
Exam Outline
The exam covers:
Section 1: Databricks SQL – 22%
● Describe the key audience and side audiences for Databricks SQL.
● Describe that a variety of users can view and run Databricks SQL dashboards as stakeholders.
● Describe the benefits of using Databricks SQL for in-Lakehouse platform data processing.
● Describe how to complete a basic Databricks SQL query.
● Identify Databricks SQL queries as a place to write and run SQL code.
● Identify the information displayed in the schema browser from the Query Editor page.
● Identify Databricks SQL dashboards as a place to display the results of multiple queries at once.
● Describe how to complete a basic Databricks SQL dashboard.
● Describe how dashboards can be configured to automatically refresh.
● Describe the purpose of Databricks SQL endpoints/warehouses.
● Identify Serverless Databricks SQL endpoint/warehouses as a quick-starting option.
● Describe the trade-off between cluster size and cost for Databricks SQL endpoints/warehouses.
● Identify Partner Connect as a tool for implementing simple integrations with a number of other data products.
● Describe how to connect Databricks SQL to ingestion tools like Fivetran.
● Identify the need to be set up with a partner to use it for Partner Connect.
● Identify small-file upload as a solution for importing small text files like lookup tables and quick data integrations.
● Import from object storage using Databricks SQL.
● Identify that Databricks SQL can ingest directories of files of the files are the same type.
● Describe how to connect Databricks SQL to visualization tools like Tableau, Power BI, and Looker.
● Identify Databricks SQL as a complementary tool for BI partner tool workflows.
● Describe the medallion architecture as a sequential data organization and pipeline system of progressively cleaner data.
● Identify the gold layer as the most common layer for data analysts using Databricks SQL.
● Describe the cautions and benefits of working with streaming data.
● Identify that the Lakehouse allows the mixing of batch and streaming workloads.
Section 2: Data Management – 20%
● Describe Delta Lake as a tool for managing data files.
● Describe that Delta Lake manages table metadata.
● Identify that Delta Lake tables maintain history for a period of time.
● Describe the benefits of Delta Lake within the Lakehouse.
● Describe persistence and scope of tables on Databricks.
● Compare and contrast the behavior of managed and unmanaged tables.
● Identify whether a table is managed or unmanaged.
● Explain how the LOCATION keyword changes the default location of database contents.
● Use Databricks to create, use, and drop databases, tables, and views.
● Describe the persistence of data in a view and a temp view
● Compare and contrast views and temp views.
● Explore, preview, and secure data using Data Explorer.
● Use Databricks to create, drop, and rename tables.
● Identify the table owner using Data Explorer.
● Change access rights to a table using Data Explorer.
● Describe the responsibilities of a table owner.
● Identify organization-specific considerations of PII data
Section 3: SQL in the Lakehouse – 29%
● Identify a query that retrieves data from the database with specific conditions
● Identify the output of a SELECT query
● Compare and contrast MERGE INTO, INSERT TABLE, and COPY INTO.
● Simplify queries using subqueries.
● Compare and contrast different types of JOINs.
● Aggregate data to achieve a desired output.
● Manage nested data formats and sources within tables.
● Use cube and roll-up to aggregate a data table.
● Compare and contrast roll-up and cube.
● Use windowing to aggregate time data.
● Identify a benefit of having ANSI SQL as the standard in the Lakehouse.
● Identify, access, and clean silver-level data.
● Utilize query history and caching to reduce development time and query latency.
● Optimize performance using higher-order Spark SQL functions.
● Create and apply UDFs in common scaling scenarios
Section 4: Data Visualization and Dashboarding – 18%
● Create basic, schema-specific visualizations using Databricks SQL.
● Identify which types of visualizations can be developed in Databricks SQL (table, details, counter, pivot).
● Explain how visualization formatting changes the reception of a visualization
● Describe how to add visual appeal through formatting
● Identify that customizable tables can be used as visualizations within Databricks SQL.
● Describe how different visualizations tell different stories.
● Create customized data visualizations to aid in data storytelling.
● Create a dashboard using multiple existing visualizations from Databricks SQL Queries.
● Describe how to change the colors of all of the visualizations in a dashboard.
● Describe how query parameters change the output of underlying queries within a dashboard
● Identify the behavior of a dashboard parameter
● Identify the use of the "Query Based Dropdown List" as a way to create a query parameter from the distinct output of a different query.
● Identify the method for sharing a dashboard with up-to-date results.
● Describe the pros and cons of sharing dashboards in different ways
● Identify that users without permission to all queries, databases, and endpoints can easily refresh a dashboard using the owner's credentials.
● Describe how to configure a refresh schedule
● Identify what happens if a refresh rate is less than the Warehouse's "Auto Stop"
● Describe how to configure and troubleshoot a basic alert
● Describe how notifications are sent when alerts are set up based on the configuration
Section 5: Analytics applications – 11%
● Compare and contrast discrete and continuous statistics.
● Describe descriptive statistics.
● Describe key moments of statistical distributions.
● Compare and contrast key statistical measures.
● Describe data enhancement as a common analytics application.
● Enhance data in a common analytics application.
● Identify a scenario in which data enhancement would be beneficial.
● Describe the blending of data between two source applications.
● Identify a scenario in which data blending would be beneficial.
● Perform last-mile ETL as project-specific data enhancement.
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1. A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every minute.
A data analyst has created a dashboard based on this gold-level data. The project stakeholders want to see the results in the dashboard updated within one minute or less of new data becoming available within the gold-level tables.
Which of the following cautions should the data analyst share prior to setting up the dashboard to complete this task?
A.The required compute resources could be costly
B.The gold-level tables are not appropriately clean for business reporting
C.The streaming data is not an appropriate data source for a dashboard
D.The streaming cluster is not fault tolerant
E.The dashboard cannot be refreshed that quickly
Answer: A
2. A data analyst has set up a SQL query to run every four hours on a SQL endpoint, but the SQL endpoint is taking too long to start up with each run.
Which of the following changes can the data analyst make to reduce the start-up time for the endpoint while managing costs?
A.Reduce the SQL endpoint cluster size
B.Increase the SQL endpoint cluster size
C.Turn off the Auto stop feature
D.Increase the minimum scaling value
E.Use a Serverless SQL endpoint
Answer: E
3. Which of the following statements about adding visual appeal to visualizations in the Visualization Editor is incorrect?
A.Visualization scale can be changed.
B.Data Labels can be formatted.
C.Colors can be changed.
D.Borders can be added.
E.Tooltips can be formatted.
Answer: D
4. In which of the following situations should a data analyst use higher-order functions?
A.When custom logic needs to be applied to simple, unnested data
B.When custom logic needs to be converted to Python-native code
C.When custom logic needs to be applied at scale to array data objects
D.When built-in functions are taking too long to perform tasks
E.When built-in functions need to run through the Catalyst Optimizer
Answer: C
5. A data analyst wants to create a dashboard with three main sections: Development, Testing, and Production. They want all three sections on the same dashboard, but they want to clearly designate the sections using text on the dashboard.
Which of the following tools can the data analyst use to designate the Development, Testing, and Production sections using text?
A.Separate endpoints for each section
B.Separate queries for each section
C.Markdown-based text boxes
D.Direct text written into the dashboard in editing mode
E.Separate color palettes for each section
Answer: C
6. Which of the following is a benefit of Databricks SQL using ANSI SQL as its standard SQL dialect?
A.It has increased customization capabilities
B.It is easy to migrate existing SQL queries to Databricks SQL
C.It allows for the use of Photon's computation optimizations
D.It is more performant than other SQL dialects
E.It is more compatible with Spark's interpreters
Answer: B
7. How can a data analyst determine if query results were pulled from the cache?
A.Go to the Query History tab and click on the text of the query. The slideout shows if the results came from the cache.
B.Go to the Alerts tab and check the Cache Status alert.
C.Go to the Queries tab and click on Cache Status. The status will be green if the results from the last run came from the cache.
D.Go to the SQL Warehouse (formerly SQL Endpoints) tab and click on Cache. The Cache file will show the contents of the cache.
E.Go to the Data tab and click Last Query. The details of the query will show if the results came from the cache.
Answer: A
8. A data analyst has created a Query in Databricks SQL, and now they want to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which of the following steps will they need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?
A.They will need to alter the Query to return two separate sets of results.
B.They will need to add two separate visualizations to the dashboard based on the same Query.
C.They will need to create two separate dashboards.
D.They will need to decide on a single data visualization to add to the dashboard.
E.They will need to copy the Query and create one data visualization per query.
Answer: B
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