Databricks Certified Associate Developer for Apache Spark 3.5 - Python : Associate-Developer-Apache-Spark-3.5

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. 21 of 55.
What is the behavior of the function date_sub(start, days) if a negative value is passed into the days parameter?

A) The number of days specified will be removed from the start date.
B) An error message of an invalid parameter will be returned.
C) The same start date will be returned.
D) The number of days specified will be added to the start date.

2. A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

A) The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
B) The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame
C) The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
D) The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors

3. A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.
Which action should the engineer take to resolve this issue?

A) Increase the memory allocated to the Spark Driver.
B) Optimize the data processing logic by repartitioning the DataFrame.
C) Modify the Spark configuration to disable garbage collection
D) Cache large DataFrames to persist them in memory.

4. 54 of 55.
What is the benefit of Adaptive Query Execution (AQE)?

A) It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
B) It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.
C) It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
D) It allows Spark to optimize the query plan before execution but does not adapt during runtime.

5. A data engineer has been asked to produce a Parquet table which is overwritten every day with the latest data. The downstream consumer of this Parquet table has a hard requirement that the data in this table is produced with all records sorted by the market_time field.
Which line of Spark code will produce a Parquet table that meets these requirements?

A) final_df \
.orderBy("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
B) final_df \
.sort("market_time") \
.coalesce(1) \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
C) final_df \
.sort("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
D) final_df \
.sortWithinPartitions("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")

Solutions: