Data-Analyst-Associate Sample Questions

Data-Analyst-Associate Sample Questions & Answers

Advanced SQL operations across the lakehouse architecture carry the top weight, alongside query optimization in the Databricks SQL service, managing Unity Catalog and Delta Lake, building dashboards and visualizations, and analytics-application development.

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Free Data-Analyst-Associate Sample Questions with Answers

Real questions from the Databricks Certified Data Analyst Associate practice test — answers and explanations included. Showing 9 of 19 free samples.

  1. Question 1IntermediateSelect 2

    Data Management · Delta Lake Time Travel

    A data governance team wants to ensure that analysts can only query a version of the customers table from exactly 7 days ago for a weekly compliance report, preventing access to any more recent data. Which Delta Lake feature allows for this specific type of historical data access? (Select TWO)

    Show answer & explanation

    Correct answers: A, C

  2. Question 2Advanced

    Analytics Applications · Optimizing Analytics Workflows

    Case Study:

    Company Background:
    Global Retail Innovations (GRI) is a large e-commerce company that uses Databricks for all its data analytics. They follow the medallion architecture, with raw event data landing in bronze tables, cleaned and enriched data in silver tables, and aggregated business-level data in gold tables. The data analytics team primarily uses Databricks SQL to build dashboards for various departments.

    Current Situation:
    The marketing department has requested a new, complex dashboard to track customer lifetime value (LTV). The primary data source for this is a large silver table named customer_transactions with over 5 billion rows. The preliminary query developed by a junior analyst to calculate LTV is taking over 30 minutes to run, which is too slow for an interactive dashboard. The query involves multiple joins with other large dimension tables (customers, products) and uses several window functions.

    Requirements:

    1. The LTV dashboard must load in under 60 seconds.
    2. The solution should not require data engineers to build a new ETL pipeline if possible.
    3. The solution must be cost-effective and leverage existing Databricks SQL capabilities.
    4. The final data presented in the dashboard must be aggregated at the customer level.

    Problem:
    How should the data analyst restructure the analytics workflow to meet the performance requirements for the LTV dashboard?

    Show answer & explanation

    Correct answer: B

    This is the best practice within the medallion architecture. For complex, slow-running queries that feed dashboards, you should pre-compute the results and store them in a gold-level aggregate table. Querying a small, pre-aggregated table will be extremely fast and easily meet the <60 second requirement. This approach is cost-effective as the expensive computation runs infrequently on a schedule, and the dashboard queries are cheap. It aligns perfectly with the purpose of the gold layer.

  3. Question 3Beginner

    Data Management · Data Ingestion using UI

    A data analyst has been given a CSV file containing quarterly sales targets. The file needs to be uploaded to Databricks and queried via SQL. The analyst does not have permissions to create external locations or configure cloud storage. What is the simplest method for the analyst to upload this file and make it queryable?

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    Correct answer: B

    The 'Upload Data' feature in the Databricks UI is the most straightforward method for users to upload small files like CSVs and create a managed Delta table from them without needing advanced permissions or knowledge of cloud storage configurations. This tool handles the file upload, schema inference, and table creation in a simple, guided workflow.

  4. Question 4Intermediate

    Databricks SQL · SQL Warehouse Configuration

    When configuring a SQL warehouse, what is the primary purpose of the 'Scaling' setting?

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    Correct answer: C

    The 'Scaling' setting allows a SQL warehouse to dynamically adjust the number of clusters it uses based on the number of concurrent queries it receives. By setting a minimum and maximum, you enable the warehouse to automatically 'scale out' by adding more clusters to handle high concurrency and 'scale in' by removing clusters during periods of low activity, balancing performance and cost.

  5. Question 5Advanced

    SQL in the Lakehouse · Query Optimization Techniques

    An analyst is examining the query history to troubleshoot a slow dashboard. They notice that a specific query, which joins a large fact table with a small dimension table, is consistently taking a long time. The query profile shows a large amount of data being shuffled across the network during the join operation. Which Databricks SQL optimization technique could most effectively mitigate this issue?

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    Correct answer: B

    A broadcast join (or map-side join) is an optimization where the smaller table is sent to every worker node that holds a partition of the larger table. This avoids the expensive shuffling of the large fact table's data across the network. While Databricks often does this automatically, using a broadcast hint ensures this strategy is used, directly addressing the shuffle-related bottleneck identified in the query profile.

  6. Question 6Beginner

    Data Visualization and Dashboarding · Choosing Visualization Types

    A marketing analyst wants to create a visualization that shows the distribution of customer ages in a simple, easy-to-understand format. The goal is to quickly see which age groups are most common. Which visualization type would be most appropriate for this purpose?

    Show answer & explanation

    Correct answer: C

    A histogram is the ideal visualization for showing the distribution of a single continuous variable, such as age. It groups the data into bins (age ranges) and displays the frequency of each bin, making it very easy to see the shape of the distribution and identify common age groups.

  7. Question 7IntermediateSelect 3

    Analytics Applications · Configuring Alerts

    A data analyst is setting up an alert to notify the sales team via Slack whenever the total sales for the past hour, as recorded in the sales_stream table, drops below $500. Which components are required to configure this alert in Databricks SQL? (Select THREE)

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    Correct answers: A, B, D

  8. Question 8Intermediate

    Data Management · Managed vs External Tables

    What is the primary difference between a managed table and an external (unmanaged) table in Databricks?

    Show answer & explanation

    Correct answer: B

    This is the key distinction. For a managed table, Databricks controls the entire lifecycle, including the data files. When you DROP TABLE, the data is deleted from cloud storage. For an external table, Databricks only manages the metadata pointer to data stored in a location you specify. DROP TABLE on an external table removes the metadata from the metastore, but the data files in your cloud storage remain untouched.

  9. Question 9AdvancedSelect 2

    Data Management · Unity Catalog Privileges

    A data team uses Unity Catalog for governance. An analyst needs to be able to create new tables within the analytics_dev schema but should not be able to create new schemas within the sandbox catalog. Which two privileges are required to meet these requirements?

    Show answer & explanation

    Correct answers: A, C

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