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Latest Associate-Developer-Apache-Spark-3.5 Test Answers - Associate-Developer-Apache-Spark-3.5 Dumps Download

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q68-Q73):

NEW QUESTION # 68
An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?

A. Use the commandspark-submitwith the-verboseflag to print the logs to the console.
B. Fetch the logs by running a Spark job with thespark-sqlCLI tool.
C. Use the Spark UI to select the stage and view the executor logs directly from the stages tab.
D. Locate the executor logs on the Spark master node, typically under the/tmpdirectory.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The Spark UI is the standard and most effective way to inspect executor logs, task time, input size, and shuffles.
From the Databricks documentation:
"You can monitor job execution via the Spark Web UI. It includes detailed logs and metrics, including task- level execution time, shuffle reads/writes, and executor memory usage."
(Source: Databricks Spark Monitoring Guide) Option A is incorrect: logs are not guaranteed to be in/tmp, especially in cloud environments.
B).-verbosehelps during job submission but doesn't give detailed executor logs.
D).spark-sqlis a CLI tool for running queries, not for inspecting logs.
Hence, the correct method is using the Spark UI # Stages tab # Executor logs.

NEW QUESTION # 69
A Spark developer is building an app to monitor task performance. They need to track the maximum task processing time per worker node and consolidate it on the driver for analysis.
Which technique should be used?

A. Configure the Spark UI to automatically collect maximum times
B. Use an RDD action like reduce() to compute the maximum time
C. Use an accumulator to record the maximum time on the driver
D. Broadcast a variable to share the maximum time among workers

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct way to aggregate information (e.g., max value) from distributed workers back to the driver is using RDD actions such asreduce()oraggregate().
From the documentation:
"To perform global aggregations on distributed data, actions likereduce()are commonly used to collect summaries such as min/max/avg." Accumulators (Option B) do not support max operations directly and are not intended for such analytics.
Broadcast (Option C) is used to send data to workers, not collect from them.
Spark UI (Option D) is a monitoring tool - not an analytics collection interface.
Final Answer: A

NEW QUESTION # 70
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, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
B. 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.
C. In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
D. In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Apache Spark's cluster mode:
"The driver program runs on the cluster's worker node instead of the client's local machine. This allows the driver to be close to the data and other executors, reducing network overhead and improving fault tolerance for production jobs." (Source: Apache Spark documentation -Cluster Mode Overview) This deployment is ideal for production environments where the job is submitted from a gateway node, and Spark manages the driver lifecycle on the cluster itself.
Option A is partially true but less specific than D.
Option B is incorrect: the driver never executes all tasks; executors handle distributed tasks.
Option C describes client mode, not cluster mode.

NEW QUESTION # 71
Which UDF implementation calculates the length of strings in a Spark DataFrame?

A. spark.udf.register("stringLength", lambda s: len(s))
B. df.select(length(col("stringColumn")).alias("length"))
C. df.withColumn("length", udf(lambda s: len(s), StringType()))
D. df.withColumn("length", spark.udf("len", StringType()))

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Option B uses Spark's built-in SQL function length(), which is efficient and avoids the overhead of a Python UDF:
from pyspark.sql.functions import length, col
df.select(length(col("stringColumn")).alias("length"))
Explanation of other options:
Option A is incorrect syntax;spark.udfis not called this way.
Option C registers a UDF but doesn't apply it in the DataFrame transformation.
Option D is syntactically valid but uses a Python UDF which is less efficient than built-in functions.
Final Answer: B

NEW QUESTION # 72
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 A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B
C. DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself
D. DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself

Answer: A

Explanation:
Comprehensive and Detailed Explanation:
Broadcast joins work by sending the smaller DataFrame to all executors, eliminating the shuffle of the larger DataFrame.
From Spark documentation:
"Broadcast joins are efficient when one DataFrame is small enough to fit in memory. Spark avoids shuffling the larger table." DataFrame B (1 GB) fits within the default threshold and should be broadcasted.
It eliminates the need to shuffle the large DataFrame A.
Final Answer: B

NEW QUESTION # 73
......

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