A data pipeline updates a Delta table every 15 minutes with new records. An analyst runs a query that aggregates this table and gets results from the cache. They need to ensure they are always seeing the latest data. What is the best approach?

Manually clear the result cache before each query using a SQL command.

Use a different cluster for each query to avoid cache reuse.

Append a random comment to the query text to force a fresh execution.

Rely on automatic cache invalidation, which will trigger a fresh query after each data update.

A data analyst needs to find a query that failed yesterday. Which filter in Query History would be most appropriate to narrow down the list?

User filter set to the analyst's own name

Time filter set to 'Last 24 hours'

A query in Query History shows a very high 'Scan bytes' and a long 'Execution time'. What common performance issue does this pattern suggest?

The query is returning too many rows to the client

The query is reading more data than necessary, likely due to missing filters or selecting too many columns

The query is waiting for other queries to finish

The query is using an inefficient join type

A team has a set of 'gold standard' queries that are run by multiple analysts throughout the day. The queries are identical and the underlying data changes only nightly. What is the best practice to minimize compute costs while ensuring analysts see fresh data?

Schedule the queries to run once in the morning and have analysts view the saved results.

Rely on Databricks' automatic result caching, which will serve repeated identical queries without re-scanning data.

Encourage analysts to run the queries on a shared cluster to benefit from disk caching.

Create separate copies of the queries for each analyst to avoid cache contention.

DatabricksCertified Data Analyst Associate

Domain 5Objective 4

Utilize Query History and Caching to Reduce Development Time and Query Latency DATA-ANALYST-ASSOCIATE Practice Questions (Page 3)

Part of the Section 5: Analyzing Queries domain, which makes up ~17% of our current practice bank. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~6–10 in this domain), expect 1–2 from this objective — we provide 28 practice questions to prepare you well beyond it. (estimate)

28questions here

6free pages

8concepts

Questions 11–15

11expert · medium
A data pipeline updates a Delta table every 15 minutes with new records. An analyst runs a query that aggregates this table and gets results from the cache. They need to ensure they are always seeing the latest data. What is the best approach?
Select an answer first
12foundation · easy
A data analyst needs to find a query that failed yesterday. Which filter in Query History would be most appropriate to narrow down the list?
Select an answer first
13foundation · easy
Which type of caching in Databricks stores data blocks on local storage to speed up repeated scans of the same data?
Select an answer first
14foundation · easy
A query in Query History shows a very high 'Scan bytes' and a long 'Execution time'. What common performance issue does this pattern suggest?
Select an answer first
15application · medium
A team has a set of 'gold standard' queries that are run by multiple analysts throughout the day. The queries are identical and the underlying data changes only nightly. What is the best practice to minimize compute costs while ensuring analysts see fresh data?
Select an answer first

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