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

1. 43 of 55.
An organization has been running a Spark application in production and is considering disabling the Spark History Server to reduce resource usage.
What will be the impact of disabling the Spark History Server in production?

A) Improved job execution speed due to reduced logging overhead
B) Prevention of driver log accumulation during long-running jobs
C) Loss of access to past job logs and reduced debugging capability for completed jobs
D) Enhanced executor performance due to reduced log size

2. 12 of 55.
A data scientist has been investigating user profile data to build features for their model. After some exploratory data analysis, the data scientist identified that some records in the user profiles contain NULL values in too many fields to be useful.
The schema of the user profile table looks like this:
user_id STRING,
username STRING,
date_of_birth DATE,
country STRING,
created_at TIMESTAMP
The data scientist decided that if any record contains a NULL value in any field, they want to remove that record from the output before further processing.
Which block of Spark code can be used to achieve these requirements?

A) filtered_users = raw_users.na.drop("any")
B) filtered_users = raw_users.dropna(how="any")
C) filtered_users = raw_users.na.drop("all")
D) filtered_users = raw_users.dropna(how="all")

3. A Spark application is experiencing performance issues in client mode because the driver is resource-constrained.
How should this issue be resolved?

A) Increase the driver memory on the client machine
B) Switch the deployment mode to local mode
C) Add more executor instances to the cluster
D) Switch the deployment mode to cluster mode

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

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

5. 23 of 55.
A data scientist is working with a massive dataset that exceeds the memory capacity of a single machine. The data scientist is considering using Apache Spark™ instead of traditional single-machine languages like standard Python scripts.
Which two advantages does Apache Spark™ offer over a normal single-machine language in this scenario? (Choose 2 answers)

A) It requires specialized hardware to run, making it unsuitable for commodity hardware clusters.
B) It has built-in fault tolerance, allowing it to recover seamlessly from node failures during computation.
C) It processes data solely on disk storage, reducing the need for memory resources.
D) It can distribute data processing tasks across a cluster of machines, enabling horizontal scalability.
E) It eliminates the need to write any code, automatically handling all data processing.

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