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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

Question 1

What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A. The operation will load all data into the driver's memory, potentially causing memory overflow
B. The operation will fail if the Pandas DataFrame exceeds 1000 rows
C. The conversion will automatically distribute the data across worker nodes
D. Data will be lost during conversion

Question 2

What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

A. A job contains multiple tasks, and each task contains multiple stages.
B. A stage contains multiple tasks, and each task contains multiple jobs.
C. A stage contains multiple jobs, and each job contains multiple tasks.
D. A job contains multiple stages, and each stage contains multiple tasks.

Question 3

A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?

A. Use an accumulator to record the maximum time on the driver
B. Broadcast a variable to share the maximum time among workers
C. Use an RDD action like reduce() to compute the maximum time
D. Configure the Spark UI to automatically collect maximum times

Question 4

A developer is trying to join two tables, sales.purchases_fct and sales.customer_dim, using the following code:

fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in the purchases_fct table that do not exist in the customer_dim table are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?

A. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')
B. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
C. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')
D. fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))

Question 5

How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:

A. Set the spark.executor.memory property to a large value.
B. Use the spark.dynamicAllocation.enabled property to scale resources dynamically.
C. Configure the application to run in cluster mode instead of local mode.
D. Increase the number of local threads based on the number of CPU cores.

Solutions: