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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q51-Q56):

NEW QUESTION # 51
A data engineer is working on the DataFrame:

(Referring to the table image: it has columns Id, Name, count, and timestamp.) Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?

A. df.select("Name").distinct()
B. df.select("Name").distinct().orderBy(df["Name"].desc())
C. df.select("Name").orderBy(df["Name"].asc())
D. df.select("Name").distinct().orderBy(df["Name"])

Answer: D

Explanation:
To extract unique values from a column and sort them alphabetically:
distinct() is required to remove duplicate values.
orderBy() is needed to sort the results alphabetically (ascending by default).
Correct code:
df.select("Name").distinct().orderBy(df["Name"])
This is directly aligned with standard DataFrame API usage in PySpark, as documented in the official Databricks Spark APIs. Option A is incorrect because it may not remove duplicates. Option C omits sorting. Option D sorts in descending order, which doesn't meet the requirement for alphabetical (ascending) order.

NEW QUESTION # 52
45 of 55.
Which feature of Spark Connect should be considered when designing an application that plans to enable remote interaction with a Spark cluster?

A. It is primarily used for data ingestion into Spark from external sources.
B. It allows for remote execution of Spark jobs.
C. It can be used to interact with any remote cluster using the REST API.
D. It provides a way to run Spark applications remotely in any programming language.

Answer: B

Explanation:
Spark Connect enables remote execution of Spark jobs by decoupling the client from the driver using the Spark Connect protocol (gRPC).
It allows users to run Spark code from different environments (like notebooks, IDEs, or remote clients) while executing jobs on the cluster.
Key Features:
Enables remote interaction between client and Spark driver.
Supports interactive development and lightweight client sessions.
Improves developer productivity without needing driver resources locally.
Why the other options are incorrect:
A: Spark Connect is not limited to ingestion tasks.
B: It allows multi-language clients (Python, Scala, etc.) but runs via Spark Connect API, not arbitrary remote code.
C: Uses gRPC protocol, not REST.
Reference:
Databricks Exam Guide (June 2025): Section "Using Spark Connect to Deploy Applications" - describes Spark Connect architecture and remote execution model.
Spark 3.5 Documentation - Spark Connect overview and client-server protocol.

NEW QUESTION # 53
How can a Spark developer ensure optimal resource utilization when running Spark jobs in Local Mode for testing?
Options:

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

Answer: A

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 # 54
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. Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
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. Use spark.read.json() with the inferSchema option set to true
D. Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.

Answer: A

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 # 55
A data analyst wants to add a column date derived from a timestamp column.
Options:

A. dates_df.withColumn("date", f.from_unixtime("timestamp")).show()
B. dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()
C. dates_df.withColumn("date", f.to_date("timestamp")).show()
D. dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()

Answer: C

Explanation:
f.to_date() converts a timestamp or string to a DateType.
Ideal for extracting the date component (year-month-day) from a full timestamp.
Example:
from pyspark.sql.functions import to_date
dates_df.withColumn("date", to_date("timestamp"))

NEW QUESTION # 56
......

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q51-Q56):

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