Databricks Data Analyst Associate Certification Sample Questions
The purpose of this Sample Question Set is to provide you with information about the Databricks Certified Data Analyst Associate exam. These sample questions will make you very familiar with both the type and the difficulty level of the questions on the Data Analyst Associate certification test. To get familiar with real exam environment, we suggest you try our Sample Databricks Data Analyst Associate Certification Practice Exam. This sample practice exam gives you the feeling of reality and is a clue to the questions asked in the actual Databricks Certified Data Analyst Associate certification exam.
These sample questions are simple and basic questions that represent likeness to the real Databricks Certified Data Analyst Associate exam questions. To assess your readiness and performance with real-time scenario based questions, we suggest you prepare with our Premium Databricks Data Analyst Associate Certification Practice Exam. When you solve real time scenario based questions practically, you come across many difficulties that give you an opportunity to improve.
Databricks Data Analyst Associate Sample Questions:
01. Regional managers must query a shared object built on sales.gold.orders in which each manager sees only the rows for the regions their account group covers. Orders change throughout the day, and managers must always see the current rows when they query.
Which object should the analyst create?
a) A materialized view per region that filters orders to that region, each granted to the matching group
b) A dynamic view that filters rows with is_account_group_member() checks per region, granted to all managers
c) A streaming table that reads orders incrementally and appends only the rows for the querying user's region
d) A temporary view that filters orders with is_account_group_member(), created in each manager's session
Which object should the analyst create?
a) A materialized view per region that filters orders to that region, each granted to the matching group
b) A dynamic view that filters rows with is_account_group_member() checks per region, granted to all managers
c) A streaming table that reads orders incrementally and appends only the rows for the querying user's region
d) A temporary view that filters orders with is_account_group_member(), created in each manager's session
02. An AI/BI dashboard is published with individual data permissions. One viewer opens it and every widget shows a permission error, while colleagues see results normally.
What is the most likely cause?
a) The viewer lacks SELECT on tables that the dashboard's datasets query
b) The SQL warehouse had auto-stopped, so it was not running when the viewer opened it
c) The viewer is in a different time zone, so the date filters exclude every row
d) The dashboard's refresh schedule has not run since the viewer last opened it
What is the most likely cause?
a) The viewer lacks SELECT on tables that the dashboard's datasets query
b) The SQL warehouse had auto-stopped, so it was not running when the viewer opened it
c) The viewer is in a different time zone, so the date filters exclude every row
d) The dashboard's refresh schedule has not run since the viewer last opened it
03. In sales.order_lines, the column discount_pct is NULL for lines sold without a discount. A manager wants the average discount across all order lines, with an undiscounted line counting as a 0% discount. The current query returns 18.4%, which the manager believes is too high:
SELECT AVG(discount_pct) FROM sales.order_lines;
Which query returns the figure the manager wants?
a) SELECT SUM(discount_pct) / COUNT(discount_pct) FROM sales.order_lines;
b) SELECT AVG(NULLIF(discount_pct, 0)) FROM sales.order_lines;
c) SELECT AVG(COALESCE(discount_pct, 0)) FROM sales.order_lines;
d) SELECT AVG(discount_pct) FROM sales.order_lines WHERE discount_pct IS NOT NULL;
SELECT AVG(discount_pct) FROM sales.order_lines;
Which query returns the figure the manager wants?
a) SELECT SUM(discount_pct) / COUNT(discount_pct) FROM sales.order_lines;
b) SELECT AVG(NULLIF(discount_pct, 0)) FROM sales.order_lines;
c) SELECT AVG(COALESCE(discount_pct, 0)) FROM sales.order_lines;
d) SELECT AVG(discount_pct) FROM sales.order_lines WHERE discount_pct IS NOT NULL;
04. Customer master data lives in an operational PostgreSQL database that an administrator has exposed through Lakehouse Federation as the foreign catalog crm_pg. Order data lives in the Delta table main.sales.orders. An analyst needs order totals by customer segment without copying the PostgreSQL data into Databricks.
Which query meets this requirement?
a) CREATE TABLE main.sales.customers AS SELECT * FROM crm_pg.public.customers, then join orders to the new table and group by segment
b) SELECT c.segment, SUM(o.amount) FROM main.sales.orders o JOIN crm_pg.public.customers c ON o.customer_id = c.customer_id
c) SELECT c.segment, SUM(o.amount) FROM sales.orders o JOIN crm_pg.public.customers c ON o.customer_id = c.customer_id GROUP BY c.segment, run with crm_pg as the current catalog
d) SELECT c.segment, SUM(o.amount) FROM main.sales.orders o JOIN crm_pg.public.customers c ON o.customer_id = c.customer_id GROUP BY c.segment
Which query meets this requirement?
a) CREATE TABLE main.sales.customers AS SELECT * FROM crm_pg.public.customers, then join orders to the new table and group by segment
b) SELECT c.segment, SUM(o.amount) FROM main.sales.orders o JOIN crm_pg.public.customers c ON o.customer_id = c.customer_id
c) SELECT c.segment, SUM(o.amount) FROM sales.orders o JOIN crm_pg.public.customers c ON o.customer_id = c.customer_id GROUP BY c.segment, run with crm_pg as the current catalog
d) SELECT c.segment, SUM(o.amount) FROM main.sales.orders o JOIN crm_pg.public.customers c ON o.customer_id = c.customer_id GROUP BY c.segment
05. The table bronze.customer_updates receives a new row every time a customer's details change, so one customer can appear many times with different addresses. The analyst needs a cleaned result with exactly one row per customer_id, holding all columns from that customer's most recent row by updated_at.
Which approach produces this result?
a) Group by customer_id and select MAX(updated_at) together with MAX of the address and every other column
b) SELECT DISTINCT * from the table, which removes the repeated rows for each customer
c) Keep the rows where ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY updated_at DESC) equals 1
d) Sort the table by updated_at in descending order and keep the first row with LIMIT 1
Which approach produces this result?
a) Group by customer_id and select MAX(updated_at) together with MAX of the address and every other column
b) SELECT DISTINCT * from the table, which removes the repeated rows for each customer
c) Keep the rows where ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY updated_at DESC) equals 1
d) Sort the table by updated_at in descending order and keep the first row with LIMIT 1
06. An analytics team needs two things. First, a pipeline that declares how bronze order data becomes cleaned silver and aggregated gold tables, with data quality expectations that drop invalid rows. Second, a nightly schedule that refreshes that pipeline, then runs a SQL query task, then refreshes a dashboard, retrying any step that fails.
Which pairing of platform components matches these two needs?
a) Delta Lake for the declarative pipeline, and Mosaic AI for the nightly schedule of tasks
b) Unity Catalog for the declarative pipeline, and Databricks SQL for the nightly schedule of tasks
c) Delta Live Tables for the declarative pipeline, and Lakeflow Jobs for the nightly schedule of tasks
d) Lakeflow Jobs for the declarative pipeline, and Delta Live Tables for the nightly schedule of tasks
Which pairing of platform components matches these two needs?
a) Delta Lake for the declarative pipeline, and Mosaic AI for the nightly schedule of tasks
b) Unity Catalog for the declarative pipeline, and Databricks SQL for the nightly schedule of tasks
c) Delta Live Tables for the declarative pipeline, and Lakeflow Jobs for the nightly schedule of tasks
d) Lakeflow Jobs for the declarative pipeline, and Delta Live Tables for the nightly schedule of tasks
07. The production table finance.gold.ledger and the scheduled job that refreshes it are both owned by an analyst who leaves the company next week. Several groups have been granted access to the table, and the finance data team will maintain it from now on.
What is the best practice for ownership of the table?
a) Keep the departing analyst as owner, because the account remains in the metastore after the person leaves
b) Make every group that has access a co-owner, so that no single principal controls the table
c) Transfer ownership to the newest member of the finance data team, who is likely to stay longest
d) Transfer ownership to a group or service principal managed by the finance data team
What is the best practice for ownership of the table?
a) Keep the departing analyst as owner, because the account remains in the metastore after the person leaves
b) Make every group that has access a co-owner, so that no single principal controls the table
c) Transfer ownership to the newest member of the finance data team, who is likely to stay longest
d) Transfer ownership to a group or service principal managed by the finance data team
08. A retail team's Tableau workbook extracts the 2-billion-row sales.transactions table every night through a SQL warehouse connection and then aggregates it into daily totals by store. The extract now runs past its overnight window and strains the Tableau server's memory.
Which change best addresses the problem?
a) Aggregate to daily store totals in a Databricks SQL materialized view and point Tableau at it
b) Write the table to CSV files in a Unity Catalog volume each night and have Tableau extract those files
c) Increase the SQL warehouse size and keep extracting the full table each night
d) Point Tableau at the table's Parquet data files in cloud storage instead of going through the SQL warehouse
Which change best addresses the problem?
a) Aggregate to daily store totals in a Databricks SQL materialized view and point Tableau at it
b) Write the table to CSV files in a Unity Catalog volume each night and have Tableau extract those files
c) Increase the SQL warehouse size and keep extracting the full table each night
d) Point Tableau at the table's Parquet data files in cloud storage instead of going through the SQL warehouse
09. Dashboard queries on the 4 TB table web_events each filter on a single account_id and take several minutes. Their query profiles show nearly every file in the table being read for each query. The warehouse is not queueing, because only a few users run these queries.
Which change addresses the root cause?
a) Move to a larger warehouse cluster size so that the full scan finishes sooner
b) Apply liquid clustering on account_id and run OPTIMIZE so that filters on it can skip most files
c) Increase the warehouse's maximum cluster count so more of these queries can run in parallel
d) Create a standard view over web_events limited to the accounts the dashboard displays
Which change addresses the root cause?
a) Move to a larger warehouse cluster size so that the full scan finishes sooner
b) Apply liquid clustering on account_id and run OPTIMIZE so that filters on it can skip most files
c) Increase the warehouse's maximum cluster count so more of these queries can run in parallel
d) Create a standard view over web_events limited to the accounts the dashboard displays
10. A bank is integrating customer data from six source systems, and source systems are added or replaced every year. Auditors require a full, traceable history of every attribute change and the source that supplied it, and the integration layer must absorb a new source without redesigning existing structures.
Which modeling technique fits this integration layer?
a) A data vault with hubs for business keys, links for relationships and satellites for attribute history
b) A star schema with one wide customer dimension updated in place from all of the sources
c) A snowflake schema that normalizes the customer dimension into sub-dimensions by attribute group
d) A single denormalized table with one row per customer, rebuilt every night from all six sources
Which modeling technique fits this integration layer?
a) A data vault with hubs for business keys, links for relationships and satellites for attribute history
b) A star schema with one wide customer dimension updated in place from all of the sources
c) A snowflake schema that normalizes the customer dimension into sub-dimensions by attribute group
d) A single denormalized table with one row per customer, rebuilt every night from all six sources
Answers:
|
Question: 01 Answer: b |
Question: 02 Answer: a |
Question: 03 Answer: c |
Question: 04 Answer: d |
Question: 05 Answer: c |
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Question: 06 Answer: c |
Question: 07 Answer: d |
Question: 08 Answer: a |
Question: 09 Answer: b |
Question: 10 Answer: a |
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