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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

A developer wants to refactor some older Spark code to leverage built-in functions introduced in Spark 3.5.0. The existing code performs array manipulations manually. Which of the following code snippets utilizes new built-in functions in Spark 3.5.0 for array operations?

A.

result_df = prices_df \
.agg(F.count_if(F.col("spot_price") >= F.lit(min_price)))
B.

result_df = prices_df \
.withColumn("valid_price", F.when(F.col("spot_price") > F.lit(min_price), 1).otherwise(0))
C.

result_df = prices_df \
.agg(F.min("spot_price"), F.max("spot_price"))
D.

result_df = prices_df \
.agg(F.count("spot_price").alias("spot_price")) \
.filter(F.col("spot_price") > F.lit("min_price"))

Question 2

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

Question 3

A data scientist has identified that some records in the user profile table contain null values in any of the fields, and such records should be removed from the dataset before processing. The schema includes fields like user_id, username, date_of_birth, created_ts, etc.
The schema of the user profile table looks like this:

Which block of Spark code can be used to achieve this requirement?
Options:

A. filtered_df = users_raw_df.na.drop(how='all', thresh=None)
B. filtered_df = users_raw_df.na.drop(how='any')
C. filtered_df = users_raw_df.na.drop(how='all')
D. filtered_df = users_raw_df.na.drop(thresh=0)

Question 4

Which feature of Spark Connect is considered when designing an application to enable remote interaction with the Spark cluster?

A. It provides a way to run Spark applications remotely in any programming language
B. It is primarily used for data ingestion into Spark from external sources
C. It can be used to interact with any remote cluster using the REST API
D. It allows for remote execution of Spark jobs

Question 5

A data engineer replaces the exact percentile() function with approx_percentile() to improve performance, but the results are drifting too far from expected values.
Which change should be made to solve the issue?

A. Decrease the first value of the percentage parameter to increase the accuracy of the percentile ranges
B. Increase the value of the accuracy parameter in order to increase the memory usage but also improve the accuracy
C. Decrease the value of the accuracy parameter in order to decrease the memory usage but also improve the accuracy
D. Increase the last value of the percentage parameter to increase the accuracy of the percentile ranges

Solutions: