A data engineer wants to be notified automatically when the percentage of null values in a Gold table exceeds a threshold. Which approach is most appropriate for setting up this monitoring?

Add a column to the Gold table that flags null values

Create a Delta Live Tables expectation that fails the pipeline when null percentage exceeds the threshold

Manually query the table each day to check the null percentage

Use a scheduled job to compute the null percentage and send an alert if it exceeds the threshold

A data engineering team wants to track the percentage of records that fail a consistency check between two columns in a Silver table over time. They need to see trends and be alerted if the failure rate spikes. What should they do?

Set up a Databricks SQL alert on the current failure count only.

Use a Delta Live Tables expectation with `onViolation='fail'` to stop the pipeline on any violation.

Check the pipeline run logs manually after each run.

Use Delta Live Tables to write data quality metrics to a Delta table and create a dashboard with alerts.

A business requirement states that if any record in the Gold sales table fails a validation rule, the entire pipeline must stop so the issue can be investigated immediately. Which invalid-record handling strategy should the data engineer choose?

Quarantine invalid records to a separate table

Ignore invalid records and continue processing

Fail the pipeline when validation fails

Drop invalid records and log a warning

DatabricksCertified Data Engineer Associate

Domain 3Objective 7

Apply Data Quality Checks and Validation Rules to Ensure Reliable Silver and Gold Datasets. DATA-ENGINEER-ASSOCIATE Practice Questions (Page 2)

Part of the Data Transformation and Modeling domain, which accounts for 22% of the DATA-ENGINEER-ASSOCIATE exam. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~8–13 in this domain), expect 1–2 from this objective — we provide 14 practice questions to prepare you well beyond it. (estimate)

14questions here

3free pages

5concepts

22%of the exam

Questions 6–10

6foundation · easy
A data engineer needs to track the number of records that fail each data quality expectation in a Delta Live Tables pipeline over time. Which Delta Live Tables feature is designed for this purpose?
Select an answer first
7foundation · easy
A data engineer is reviewing a Silver table and finds that the same customer_id appears multiple times with different addresses. Which data quality dimension is most directly violated?
Select an answer first
8foundation · easy
A data engineer wants to be notified automatically when the percentage of null values in a Gold table exceeds a threshold. Which approach is most appropriate for setting up this monitoring?
Select an answer first
9application · medium
A data engineering team wants to track the percentage of records that fail a consistency check between two columns in a Silver table over time. They need to see trends and be alerted if the failure rate spikes. What should they do?
Select an answer first
10foundation · easy
A business requirement states that if any record in the Gold sales table fails a validation rule, the entire pipeline must stop so the issue can be investigated immediately. Which invalid-record handling strategy should the data engineer choose?
Select an answer first

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Apply Data Quality Checks and Validation Rules to Ensure Reliable Silver and Gold Datasets. DATA-ENGINEER-ASSOCIATE Practice Questions (Page 2)

Questions 6–10

A data engineer needs to track the number of records that fail each data quality expectation in a Delta Live Tables pipeline over time. Which Delta Live Tables feature is designed for this purpose?

A data engineer is reviewing a Silver table and finds that the same customer_id appears multiple times with different addresses. Which data quality dimension is most directly violated?

A data engineer wants to be notified automatically when the percentage of null values in a Gold table exceeds a threshold. Which approach is most appropriate for setting up this monitoring?

A data engineering team wants to track the percentage of records that fail a consistency check between two columns in a Silver table over time. They need to see trends and be alerted if the failure rate spikes. What should they do?

A business requirement states that if any record in the Gold sales table fails a validation rule, the entire pipeline must stop so the issue can be investigated immediately. Which invalid-record handling strategy should the data engineer choose?

Keep studying — Data Transformation and Modeling