Databricks Data Engineer Pipeline Monitoring Quiz

1. What is schema enforcement in Delta Lake?

Validation that incoming data matches the table schema

Automatic conversion of all data types

Removal of data that doesn't fit

Encryption of schema metadata

2. What is the primary benefit of using Databricks Notebooks for pipeline development?

Interactive development with real-time feedback and visualization

Eliminates the need for testing

Reduces storage requirements

Prevents code version control

3. How should you handle sensitive data in a monitored Databricks pipeline?

Use encryption, masking, and role-based access controls

Log all sensitive data to monitoring dashboards

Store sensitive data in plain text files

Avoid monitoring sensitive data entirely

4. In Databricks, what is the purpose of the _delta_log directory?

Stores transaction logs and metadata for Delta tables

Stores raw data files

Caches query results

Encrypts sensitive information

5. Which metrics should be included in a comprehensive pipeline monitoring dashboard?

Execution time, success rate, data volume, and error frequency

Only execution time

Storage size exclusively

User count only

6. What is the recommended way to schedule recurring Databricks jobs?

Use Databricks Workflows with cron scheduling

Write custom scheduling scripts only

Trigger jobs manually each time

Use external cron jobs without monitoring

7. How can you prevent data duplication in a Databricks ETL pipeline?

Use MERGE operations and deduplication logic

Process data only once manually

Avoid using Delta Lake

Disable transaction logging

8. In Databricks, what does idempotency mean for pipeline operations?

Running the operation multiple times produces the same result

The operation can only run once

Data is processed in random order

The pipeline never fails

9. Which Apache Spark concept is essential for optimizing pipeline performance?

Partitioning and bucketing

Increasing cluster size indefinitely

Using only single-threaded processing

Avoiding caching entirely

10. How should you handle data quality issues in a production Databricks pipeline?

Implement quality checks, log failures, and trigger alerts

Ignore quality issues and continue processing

Stop the entire pipeline manually

Archive bad data without investigation

11. What is the primary purpose of Delta Lake in Databricks?

Provide ACID transactions and data versioning on data lakes

Replace Apache Spark entirely

Enable only batch processing workflows

Eliminate the need for data quality checks

12. Which Databricks feature should be used to monitor pipeline costs?

Databricks Admin Console and billing dashboards

Apache Spark logs only

Manual spreadsheet tracking

Cluster naming conventions

13. In Databricks, what does OPTIMIZE do for Delta tables?

Compacts small files and removes deleted data

Increases table size

Adds new columns automatically

Encrypts sensitive data

14. What is the purpose of Delta Lake's time-travel feature?

Query data at previous points in time for auditing and recovery

Schedule future data processing jobs

Predict future data trends

Optimize cluster performance

15. Which command is used to check Delta table history in Databricks?

DESCRIBE HISTORY

SHOW HISTORY

SELECT HISTORY

FETCH HISTORY

16. How does Databricks Workflows improve pipeline reliability?

Automatic retries, dependency management, and alerting

Removes the need for error handling

Eliminates cluster failures

Reduces data storage costs only

17. What is the recommended approach for handling late-arriving data in a streaming pipeline?

Use watermarking and allowed lateness thresholds

Reject all late data immediately

Store late data in a separate unrelated table

Manually process late data weekly

18. Which monitoring metric is most critical for detecting pipeline failures in Databricks?

Job execution time and error logs

Storage capacity only

Cluster uptime percentage

User login frequency

19. In a Databricks pipeline, what does the DAG (Directed Acyclic Graph) represent?

The sequence and dependencies of pipeline tasks

The storage format of data files

The cluster configuration settings

The security permissions for notebooks

20. Which Databricks feature allows you to track data lineage and monitor data quality automatically?

Databricks SQL

Databricks Unity Catalog

Databricks Jobs

Databricks Repos

Databricks Data Engineer Data Pipeline Monitoring Quiz