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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. 33 of 55.
The data engineering team created a pipeline that extracts data from a transaction system.
The transaction system stores timestamps in UTC, and the data engineers must now transform the transaction_datetime field to the "America/New_York" timezone for reporting.
Which code should be used to convert the timestamp to the target timezone?

A) raw.withColumn("transaction_datetime", date_format(col("transaction_datetime"), "America/New_York"))
B) raw.withColumn("transaction_datetime", from_utc_timestamp(col("transaction_datetime"), "America/New_York"))
C) raw.withColumn("transaction_datetime", convert_timezone(col("transaction_datetime"), "America/New_York"))
D) raw.withColumn("transaction_datetime", to_utc_timestamp(col("transaction_datetime"), "America/New_York"))

2. 8 of 55.
A data scientist at a large e-commerce company needs to process and analyze 2 TB of daily customer transaction data. The company wants to implement real-time fraud detection and personalized product recommendations.
Currently, the company uses a traditional relational database system, which struggles with the increasing data volume and velocity.
Which feature of Apache Spark effectively addresses this challenge?

A) Built-in machine learning libraries
B) Ability to process small datasets efficiently
C) Support for SQL queries on structured data
D) In-memory computation and parallel processing capabilities

3. A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?

A) In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
B) In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
C) In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
D) In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.

4. 47 of 55.
A data engineer has written the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv")
df2 = spark.read.csv("product_data.csv")
df_joined = df1.join(df2, df1.product_id == df2.product_id)
The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.
Which join strategy will Spark use?

A) Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.
B) Shuffle join, because AQE is not enabled, and Spark uses a static query plan.
C) Shuffle join because no broadcast hints were provided.
D) Broadcast join, as df2 is smaller than the default broadcast threshold.

5. 2 of 55. Which command overwrites an existing JSON file when writing a DataFrame?

A) df.write.option("overwrite").json("path/to/file")
B) df.write.mode("append").json("path/to/file")
C) df.write.mode("overwrite").json("path/to/file")
D) df.write.json("path/to/file")

Solutions: