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

1. 16 of 55.
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior? (Choose 2 answers)

A) Transformations are executed immediately to build the lineage graph.
B) Only actions trigger the execution of the transformation pipeline.
C) The Spark engine requires manual intervention to start executing transformations.
D) Transformations are evaluated lazily.
E) The Spark engine optimizes the execution plan during the transformations, causing delays.

2. 38 of 55.
A data engineer is working with Spark SQL and has a large JSON file stored at /data/input.json.
The file contains records with varying schemas, and the engineer wants to create an external table in Spark SQL that:
Reads directly from /data/input.json.
Infers the schema automatically.
Merges differing schemas.
Which code snippet should the engineer use?

A) CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeAll 'true');
B) CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', mergeSchema 'true');
C) CREATE TABLE users
USING json
OPTIONS (path '/data/input.json');
D) CREATE EXTERNAL TABLE users
USING json
OPTIONS (path '/data/input.json', inferSchema 'true');

3. 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"))

4. 2 of 55. Which command overwrites an existing JSON file when writing a DataFrame?

A) df.write.mode("append").json("path/to/file")
B) df.write.json("path/to/file")
C) df.write.mode("overwrite").json("path/to/file")
D) df.write.option("overwrite").json("path/to/file")

5. A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?

A) In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
B) In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
C) In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
D) In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.

Solutions: