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Associate-Developer-Apache-Spark-3.5 Updated CBT, Associate-Developer-Apache-Spark-3.5 Actual Exam

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q17-Q22):

NEW QUESTION # 17
A developer is trying to join two tables,sales.purchases_fctandsales.customer_dim, using the following code:

fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in thepurchases_fcttable that do not exist in thecustomer_dimtable are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?

A. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
B. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')
C. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')
D. fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Spark, the default join type is an inner join, which returns only the rows with matching keys in both DataFrames. To retain all records from the left DataFrame (purch_df) and include matching records from the right DataFrame (cust_df), a left outer join should be used.
By specifying the join type as'left', the modified code ensures that all records frompurch_dfare preserved, and matching records fromcust_dfare included. Records inpurch_dfwithout a corresponding match incust_dfwill havenullvalues for the columns fromcust_df.
This approach is consistent with standard SQL join operations and is supported in PySpark's DataFrame API.

NEW QUESTION # 18
A data scientist wants each record in the DataFrame to contain:
The first attempt at the code does read the text files but each record contains a single line. This code is shown below:

The entire contents of a file
The full file path
The issue: reading line-by-line rather than full text per file.
Code:
corpus = spark.read.text("/datasets/raw_txt/*")
.select('*','_metadata.file_path')
Which change will ensure one record per file?
Options:

A. Add the option wholetext=False to the text() function
B. Add the option lineSep=", " to the text() function
C. Add the option wholetext=True to the text() function
D. Add the option lineSep=' ' to the text() function

Answer: C

Explanation:
To read each file as a single record, use:
spark.read.text(path, wholetext=True)
This ensures that Spark reads the entire file contents into one row.
Reference:Spark read.text() with wholetext

NEW QUESTION # 19
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. groupBy
C. filter
D. select

Answer: B

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 # 20
A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set forspark.sql.
adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

A. A shuffled hash join
B. A Cartesian join
C. A broadcast nested loop join
D. A sort-merge join

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Adaptive Query Execution (AQE) dynamically selects join strategies based on actual data sizes at runtime. If the size of post-shuffle partitions is below the threshold set by:
spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold
then Spark prefers to use a shuffled hash join.
From the Spark documentation:
"AQE selects a shuffled hash join when the size of post-shuffle data is small enough to fit within the configured threshold, avoiding more expensive sort-merge joins." Therefore:
A is wrong - Cartesian joins are only used with no join condition.
B is correct - this is the optimized join for small partitioned shuffle data under AQE.
C and D are used under other scenarios but not for this case.
Final Answer: B

NEW QUESTION # 21
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)

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("location", "path/to/destination/dir")
D. .format("parquet")
.option("path", "path/to/destination/dir")

Answer: D

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 # 22
......

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