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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q40-Q45):

NEW QUESTION # 40
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

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

Answer: B

Explanation:
A Sparkjobis triggered by an action (e.g., count, show).
The job is broken intostages, typically one per shuffle boundary.
Eachstageis divided into multipletasks, which are distributed across worker nodes.
Reference:Spark Execution Model

NEW QUESTION # 41
Given the code:

df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp"), " ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?

A. When the show action is applied
B. When the groupBy transformation is applied
C. When the count action is applied
D. When the filter transformation is applied

Answer: C

Explanation:
Spark uses lazy evaluation. Transformations like filter, select, and groupBy only define the DAG (Directed Acyclic Graph). No execution occurs until an action is triggered.
The first action in the code is:reduced_df.count()
So Spark starts processing data at this line.

NEW QUESTION # 42
Given the schema:

event_ts TIMESTAMP,
sensor_id STRING,
metric_value LONG,
ingest_ts TIMESTAMP,
source_file_path STRING
The goal is to deduplicate based on: event_ts, sensor_id, and metric_value.
Options:

A. dropDuplicates with no arguments (removes based on all columns)
B. groupBy without aggregation (invalid use)
C. dropDuplicates on the exact matching fields
D. dropDuplicates on all columns (wrong criteria)

Answer: C

Explanation:
dedup_df = iot_bronze_df.dropDuplicates(["event_ts","sensor_id","metric_value"]) dropDuplicates accepts a list of columns to use for deduplication.
This ensures only unique records based on the specified keys are retained.
Reference:DataFrame.dropDuplicates() API

NEW QUESTION # 43
A Spark application is experiencing performance issues in client mode because the driver is resource-constrained.
How should this issue be resolved?

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

Answer: B

Explanation:
In Spark's client mode, the driver runs on the local machine that submitted the job. If that machine is resource-constrained (e.g., low memory), performance degrades.
From the Spark documentation:
"In cluster mode, the driver runs inside the cluster, benefiting from cluster resources and scalability." Option A is incorrect - executors do not help the driver directly.
Option B might help short-term but does not scale.
Option C is correct - switching to cluster mode moves the driver to the cluster.
Option D (local mode) is for development/testing, not production.
Final answer: C

NEW QUESTION # 44
A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFramedfwith columnsuser_id,product_id, andpurchase_amountand needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

A. df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
B. df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
C. df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
D. df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Shuffling occurs in operations likegroupBy,reduceByKey, orjoin-which cause data to be moved across partitions. Therepartition()operation can also cause a shuffle, but in this context, it follows an aggregation.
InOption D, thegroupByfollowed byaggresults in a shuffle due to grouping across nodes.
Therepartition(10)is a partitioning transformation but does not involve a new shuffle since the data is already grouped.
This sequence - shuffle (groupBy) followed by non-shuffling (repartition) - is correct.
Option A does the opposite: thefilterdoes not cause a shuffle, butgroupBydoes - this makes it the wrong order.

NEW QUESTION # 45
......

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