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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q125-Q130):

NEW QUESTION # 125
A data engineer is working with a large JSON dataset containing order information. The dataset is stored in a distributed file system and needs to be loaded into a Spark DataFrame for analysis. The data engineer wants to ensure that the schema is correctly defined and that the data is read efficiently.
Which approach should the data scientist use to efficiently load the JSON data into a Spark DataFrame with a predefined schema?

A. Use spark.read.json() with the inferSchema option set to true
B. Use spark.read.json() to load the data, then use DataFrame.printSchema() to view the inferred schema, and finally use DataFrame.cast() to modify column types.
C. Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
D. Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.

Answer: C

Explanation:
The most efficient and correct approach is to define a schema using StructType and pass it tospark.read.
schema(...).
This avoids schema inference overhead and ensures proper data types are enforced during read.
Example:
frompyspark.sql.typesimportStructType, StructField, StringType, DoubleType schema = StructType([ StructField("order_id", StringType(),True), StructField("amount", DoubleType(),True),
])
df = spark.read.schema(schema).json("path/to/json")
- Source:Databricks Guide - Read JSON with predefined schema

NEW QUESTION # 126
A developer wants to refactor some older Spark code to leverage built-in functions introduced in Spark 3.5.0. The existing code performs array manipulations manually. Which of the following code snippets utilizes new built-in functions in Spark 3.5.0 for array operations?

A.
result_df = prices_df
.agg(F.min("spot_price"), F.max("spot_price"))
B.
result_df = prices_df
.agg(F.count_if(F.col("spot_price") >= F.lit(min_price)))
C.
result_df = prices_df
.withColumn("valid_price", F.when(F.col("spot_price") > F.lit(min_price), 1).otherwise(0))
D.
result_df = prices_df
.agg(F.count("spot_price").alias("spot_price"))
.filter(F.col("spot_price") > F.lit("min_price"))

Answer: B

Explanation:
count_if(condition) counts the number of rows that meet the specified boolean condition.
In this example, it directly counts how many times spot_price >= min_price evaluates to true, replacing the older verbose combination of when/otherwise and filtering or summing.
Official Spark 3.5.0 documentation notes the addition of count_if to simplify this kind of logic:
"Added count_if aggregate function to count only the rows where a boolean condition holds (SPARK-43773)." Why other options are incorrect or outdated:
A uses a legacy-style method of adding a flag column (when().otherwise()), which is verbose compared to count_if.
C performs a simple min/max aggregation-useful but unrelated to conditional array operations or the updated functionality.
D incorrectly applies .filter() after .agg() which will cause an error, and misuses string "min_price" rather than the variable.
Therefore, B is the only option leveraging new functionality from Spark 3.5.0 correctly and efficiently.
Explanation:
The correct answer is B because it uses the new function count_if, introduced in Spark 3.5.0, which simplifies conditional counting within aggregations.

NEW QUESTION # 127
How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:

A. Set the spark.executor.memory property to a large value.
B. Increase the number of local threads based on the number of CPU cores.
C. Use the spark.dynamicAllocation.enabled property to scale resources dynamically.
D. Configure the application to run in cluster mode instead of local mode.

Answer: B

Explanation:
When running in local mode (e.g., local[4]), the number inside the brackets defines how many threads Spark will use.
Using local[*] ensures Spark uses all available CPU cores for parallelism.
Example:
spark-submit --master local[*]
Dynamic allocation and executor memory apply to cluster-based deployments, not local mode.

NEW QUESTION # 128
24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?

A. spark.read.parquet("events.parquet").printSchema()
B. spark.sql("SELECT schema FROM events.parquet").show()
C. spark.read.format("parquet").load("events.parquet").show()
D. spark.sql("SELECT * FROM events.parquet").show()

Answer: A

Explanation:
To view the schema of a Parquet file, you must use the DataFrameReader to load the Parquet data and call the .printSchema() method.
Correct syntax:
spark.read.parquet("events.parquet").printSchema()
This command loads the file metadata (without triggering a full read) and prints the column names, data types, and nullability information in a tree format.
Why the other options are incorrect:
A/D: SQL queries can't directly introspect file schemas.
B: .show() displays data rows, not schema.
Reference:
PySpark DataFrameReader API - read.parquet() and DataFrame.printSchema().
Databricks Exam Guide (June 2025): Section "Using Spark SQL" - describes reading files and examining schemas in Spark SQL and DataFrame APIs.

NEW QUESTION # 129
36 of 55.
What is the main advantage of partitioning the data when persisting tables?

A. It automatically cleans up unused partitions to optimize storage.
B. It compresses the data to save disk space.
C. It optimizes by reading only the relevant subset of data from fewer partitions.
D. It ensures that data is loaded into memory all at once for faster query execution.

Answer: C

Explanation:
Partitioning a dataset divides data into separate directories based on partition column values. When queries filter on partitioned columns, Spark can prune irrelevant partitions - meaning it only reads files that match the filter criteria.
Advantage:
Reduces I/O and improves performance by scanning only relevant subsets of data.
Example:
/data/sales/year=2023/month=10/...
/data/sales/year=2024/month=01/...
A query filtering WHERE year = 2024 reads only the relevant partition.
Why the other options are incorrect:
A: Compression is independent of partitioning.
B: Spark does not automatically clean partitions unless managed manually.
C: Partitioning does not cause Spark to load entire data into memory.
Reference:
Databricks Exam Guide (June 2025): Section "Using Spark SQL" - partitioning and pruning for optimized data retrieval.
Spark SQL Documentation - DataFrameWriter partitionBy() and query optimization.

NEW QUESTION # 130
......

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