1500 Questions | Databricks Spark 3.0 Associate Developer

Data Engineering on Delta Lake (30%): Managing file formats, leveraging data versioning (Time Travel), maintaining data history, and ensuring high data quality within the Lakehouse architecture.

Data Engineering with Apache Spark (20%): Core Spark architecture, RDD transformations, and building robust data ingestion pipelines.

Data Warehousing and ETL (20%): Implementing ETL processes at scale, integrating diverse data sources, and managing big data workloads in cloud storage environments.

B. Filter()

C. GroupBy()

D. Map()

E. WithColumn()

F. Drop()

Correct Answer: C

Explanation:

A (Incorrect): select() is a narrow transformation as it only operates on columns within the same partition.

B (Incorrect): filter() simply removes rows within a partition without needing data from other partitions.

D (Incorrect): map() processes each element independently within its original partition.

E (Incorrect): withColumn() adds or replaces a column locally within the partition.

F (Incorrect): drop() is a metadata-level operation that happens within the partition.

Question 2: In Delta Lake, which command allows a developer to view and restore a previous version of a table to recover from accidental data loss?

B. DESCRIBE HISTORY

C. UNDO COMMIT

D. ROLLBACK DATA

E. REVERT TO VERSION

F. TABLE RECOVERY

Correct Answer: B

Explanation:

A (Incorrect): While "RESTORE" is the action, DESCRIBE HISTORY is the fundamental command to identify the target state.

C (Incorrect): UNDO COMMIT is not a valid Spark/Delta command.

D (Incorrect): ROLLBACK is a SQL term but not the specific command for Delta versioning management.

E (Incorrect): This is a descriptive phrase, not the actual command used in the API.

F (Incorrect): This is not a recognized Delta Lake command.

Question 3: A developer needs to improve the performance of a join operation between a large fact table and a very small dimension table. Which technique is most appropriate?

B. Using a Broadcast Join.

C. Increasing the number of executors.

D. Converting the small table to an RDD.

E. Disabling the Spark UI.

F. Using a SortMerge Join.

Correct Answer: B

Explanation:

A (Incorrect): Repartitioning both tables is expensive and unnecessary if one table is small enough to fit in memory.

C (Incorrect): While more resources help, they don't fix an inefficient join strategy.

D (Incorrect): Converting to RDD usually decreases performance due to the loss of Catalyst Optimizer benefits.

E (Incorrect): Disabling the UI has no impact on processing logic or speed.

F (Incorrect): SortMergeJoin is better for two large tables but slower than a BroadcastJoin for this specific scenario.

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