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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:

1. 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.

2. 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) Add more executor instances to the cluster
C) Switch the deployment mode to local mode
D) Switch the deployment mode to cluster mode

3. An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
from pyspark.sql.functions import broadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

A) It ensures that the join happens only when the id values are identical.
B) It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
C) It increases the partition size for df1 and df2.
D) It filters the id values before performing the join.

4. You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

A) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A
B) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
C) DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself
D) DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B

5. A data engineer writes the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)

Which join strategy will Spark use?

A) Shuffle join because no broadcast hints were provided
B) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently
C) Shuffle join, because AQE is not enabled, and Spark uses a static query plan
D) Broadcast join, as df2 is smaller than the default broadcast threshold

Solutions: