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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q114-Q119):

NEW QUESTION # 114
Which UDF implementation calculates the length of strings in a Spark DataFrame?

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

Answer: B

Explanation:
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.udf is 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 # 115
4 of 55.
A developer is working on a Spark application that processes a large dataset using SQL queries. Despite having a large cluster, the developer notices that the job is underutilizing the available resources. Executors remain idle for most of the time, and logs reveal that the number of tasks per stage is very low. The developer suspects that this is causing suboptimal cluster performance.
Which action should the developer take to improve cluster utilization?

A. Increase the value of spark.sql.shuffle.partitions
B. Reduce the value of spark.sql.shuffle.partitions
C. Increase the size of the dataset to create more partitions
D. Enable dynamic resource allocation to scale resources as needed

Answer: A

Explanation:
In Spark SQL and DataFrame operations, the configuration parameter spark.sql.shuffle.partitions defines the number of partitions created during shuffle operations such as join, groupBy, and distinct.
The default value (in Spark 3.5) is 200.
If this number is too low, Spark creates fewer tasks, leading to idle executors and poor cluster utilization.
Increasing this value allows Spark to create more tasks that can run in parallel across executors, effectively using more cluster resources.
Correct approach:
spark.conf.set("spark.sql.shuffle.partitions", 400)
This increases the parallelism level of shuffle stages and improves overall resource utilization.
Why the other options are incorrect:
B: Reducing partitions further would decrease parallelism and worsen the underutilization issue.
C: Dynamic resource allocation scales executors up or down based on workload, but it doesn't fix low task parallelism caused by insufficient shuffle partitions.
D: Increasing dataset size is not a tuning solution and doesn't address task-level under-parallelization.
Reference (Databricks Apache Spark 3.5 - Python / Study Guide):
Spark SQL Configuration: spark.sql.shuffle.partitions - controls the number of shuffle partitions.
Databricks Exam Guide (June 2025): Section "Troubleshooting and Tuning Apache Spark DataFrame API Applications" - tuning strategies, partitioning, and optimizing cluster utilization.

NEW QUESTION # 116
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 because the driver does not have enough CPU cores to serialize the large DataFrame
B. The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
C. The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
D. The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors

Answer: C

Explanation:
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.

NEW QUESTION # 117
A data engineer noticed improved performance after upgrading from Spark 3.0 to Spark 3.5. The engineer found that Adaptive Query Execution (AQE) was enabled.
Which operation is AQE implementing to improve performance?

A. Improving the performance of single-stage Spark jobs
B. Optimizing the layout of Delta files on disk
C. Collecting persistent table statistics and storing them in the metastore for future use
D. Dynamically switching join strategies

Answer: D

Explanation:
Adaptive Query Execution (AQE) is a Spark 3.x feature that dynamically optimizes query plans at runtime. One of its core features is:
Dynamically switching join strategies (e.g., from sort-merge to broadcast) based on runtime statistics.
Other AQE capabilities include:
Coalescing shuffle partitions
Skew join handling
Option A is correct.
Option B refers to statistics collection, which is not AQE's primary function.
Option C is too broad and not AQE-specific.
Option D refers to Delta Lake optimizations, unrelated to AQE.
Final answer: A

NEW QUESTION # 118
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 lineSep=' ' to the text() function
B. Add the option wholetext=False 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 # 119
......

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