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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q50-Q55):

NEW QUESTION # 50
An engineer has a large ORC file located at/file/test_data.orcand wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e.,col1,col2, during the reading process?

A. spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")
B. spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")
C. spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")
D. spark.read.orc("/file/test_data.orc").selected("col1", "col2")

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct way to load specific columns from an ORC file is to first load the file using.load()and then apply.
select()on the resulting DataFrame. This is valid with.read.format("orc")or the shortcut.read.orc().
df = spark.read.format("orc").load("/file/test_data.orc").select("col1","col2") Why others are incorrect:
Aperforms selection after filtering, but doesn't match the intention to minimize memory at load.
Bincorrectly tries to use.select()before.load(), which is invalid.
Cuses a non-existent.selected()method.
Dcorrectly loads and then selects.
Reference:Apache Spark SQL API - ORC Format

NEW QUESTION # 51
A data engineer replaces the exact percentile() function with approx_percentile() to improve performance, but the results are drifting too far from expected values.
Which change should be made to solve the issue?

A. Increase the last value of the percentage parameter to increase the accuracy of the percentile ranges
B. Increase the value of the accuracy parameter in order to increase the memory usage but also improve the accuracy
C. Decrease the first value of the percentage parameter to increase the accuracy of the percentile ranges
D. Decrease the value of the accuracy parameter in order to decrease the memory usage but also improve the accuracy

Answer: B

Explanation:
Comprehensive and Detailed Explanation:
The approx_percentile function in Spark is a performance-optimized alternative to percentile. It takes an optional accuracy parameter:
approx_percentile(column, percentage, accuracy)
Higher accuracy values # more precise results, but increased memory/computation.
Lower values # faster but less accurate.
From the documentation:
"Increasing the accuracy improves precision but increases memory usage." Final Answer: D

NEW QUESTION # 52
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. Only actions trigger the execution of the transformation pipeline.
B. The Spark engine optimizes the execution plan during the transformations, causing delays.
C. Transformations are executed immediately to build the lineage graph.
D. The Spark engine requires manual intervention to start executing transformations.
E. Transformations are evaluated lazily.

Answer: A,E

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Apache Spark employs a lazy evaluation model for transformations. This means that when transformations (e.
g.,map(),filter()) are applied to a DataFrame, Spark does not execute them immediately. Instead, it builds a logical plan (lineage) of transformations to be applied.
Execution is deferred until an action (e.g.,collect(),count(),save()) is called. At that point, Spark's Catalyst optimizer analyzes the logical plan, optimizes it, and then executes the physical plan to produce the result.
This lazy evaluation strategy allows Spark to optimize the execution plan, minimize data shuffling, and improve overall performance by reducing unnecessary computations.

NEW QUESTION # 53
A data analyst builds a Spark application to analyze finance data and performs the following operations:filter, select,groupBy, andcoalesce.
Which operation results in a shuffle?

A. coalesce
B. filter
C. select
D. groupBy

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
ThegroupBy()operation causes a shuffle because it requires all values for a specific key to be brought together, which may involve moving data across partitions.
In contrast:
filter()andselect()are narrow transformations and do not cause shuffles.
coalesce()tries to reduce the number of partitions and avoids shuffling by moving data to fewer partitions without a full shuffle (unlikerepartition()).
Reference:Apache Spark - Understanding Shuffle

NEW QUESTION # 54
A data engineer needs to write a Streaming DataFrame as Parquet files.
Given the code:

Which code fragment should be inserted to meet the requirement?
A)

B)

C)

D)

Which code fragment should be inserted to meet the requirement?

A. CopyEdit
.option("format", "parquet")
.option("destination", "path/to/destination/dir")
B. .option("format", "parquet")
.option("location", "path/to/destination/dir")
C. .format("parquet")
.option("path", "path/to/destination/dir")
D. .format("parquet")
.option("location", "path/to/destination/dir")

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To write a structured streaming DataFrame to Parquet files, the correct way to specify the format and output directory is:
writeStream
format("parquet")
option("path", "path/to/destination/dir")
According to Spark documentation:
"When writing to file-based sinks (like Parquet), you must specify the path using the .option("path", ...) method. Unlike batch writes, .save() is not supported." Option A incorrectly uses.option("location", ...)(invalid for Parquet sink).
Option B incorrectly sets the format via.option("format", ...), which is not the correct method.
Option C repeats the same issue.
Option D is correct:.format("parquet")+.option("path", ...)is the required syntax.
Final Answer: D

NEW QUESTION # 55
......

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Test Associate-Developer-Apache-Spark-3.5 Questions Vce - Successfully Pass The Databricks Certified Associate Developer for Apache Spark 3.5 - Python

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q50-Q55):

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