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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q70-Q75):

NEW QUESTION # 70
A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?

A. The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
B. The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame
C. The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors
D. The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Apache Spark, broadcast variables are used to efficiently distribute large, read-only data to all worker nodes. However, broadcasting very large datasets can lead to memory issues on executors if the data does not fit into the available memory.
According to the Spark documentation:
"Broadcast variables allow the programmer to keep a read-only variable cached on each machine rather than shipping a copy of it with tasks. This can greatly reduce the amount of data sent over the network." However, it also notes:
"Using the broadcast functionality available in SparkContext can greatly reduce the size of each serialized task, and the cost of launching a job over a cluster. If your tasks use any large object from the driver program inside of them (e.g., a static lookup table), consider turning it into a broadcast variable." But caution is advised when broadcasting large datasets:
"Broadcasting large variables can cause out-of-memory errors if the data does not fit in the memory of each executor." Therefore, if the broadcasted DataFrame containing millions of rows exceeds the memory capacity of the executors, the job may fail due to memory constraints.
Reference:Spark 3.5.5 Documentation - Tuning

NEW QUESTION # 71
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. Transformations are executed immediately to build the lineage graph.
C. The Spark engine optimizes the execution plan during the transformations, causing delays.
D. Transformations are evaluated lazily.
E. The Spark engine requires manual intervention to start executing transformations.

Answer: A,D

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 # 72
A developer runs:

What is the result?
Options:

A. It throws an error if there are null values in either partition column.
B. It appends new partitions to an existing Parquet file.
C. It stores all data in a single Parquet file.
D. It creates separate directories for each unique combination of color and fruit.

Answer: D

Explanation:
ThepartitionBy()method in Spark organizes output into subdirectories based on unique combinations of the specified columns:
e.g.
/path/to/output/color=red/fruit=apple/part-0000.parquet
/path/to/output/color=green/fruit=banana/part-0001.parquet
This improves query performance via partition pruning.
It does not consolidate into a single file.
Null values are allowed in partitions.
It does not "append" unless.mode("append")is used.
Reference:Spark Write with Partitioning

NEW QUESTION # 73
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=True to the text() function
B. Add the option lineSep=' ' to the text() function
C. Add the option wholetext=False to the text() function
D. Add the option lineSep=", " to the text() function

Answer: A

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 # 74
What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A. The conversion will automatically distribute the data across worker nodes
B. The operation will fail if the Pandas DataFrame exceeds 1000 rows
C. The operation will load all data into the driver's memory, potentially causing memory overflow
D. Data will be lost during conversion

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When you convert a largepyspark.pandas(aka Pandas API on Spark) DataFrame to a local Pandas DataFrame using.toPandas(), Spark collects all partitions to the driver.
From the Spark documentation:
"Be careful when converting large datasets to Pandas. The entire dataset will be pulled into the driver's memory." Thus, for large datasets, this can cause memory overflow or out-of-memory errors on the driver.
Final Answer: D

NEW QUESTION # 75
......

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