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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. A data engineer is building an Apache Spark™ Structured Streaming application to process a stream of JSON events in real time. The engineer wants the application to be fault-tolerant and resume processing from the last successfully processed record in case of a failure. To achieve this, the data engineer decides to implement checkpoints.
Which code snippet should the data engineer use?

A) query = streaming_df.writeStream \
.format("console") \
.option("checkpoint", "/path/to/checkpoint") \
.outputMode("append") \
.start()
B) query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.start()
C) query = streaming_df.writeStream \
.format("console") \
.outputMode("complete") \
.start()
D) query = streaming_df.writeStream \
.format("console") \
.outputMode("append") \
.option("checkpointLocation", "/path/to/checkpoint") \
.start()

2. A data scientist is working with a Spark DataFrame called customerDF that contains customer information. The DataFrame has a column named email with customer email addresses. The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?

A) customerDF.select(
regexp_replace(col("email"), "@", "").alias("username"),
regexp_replace(col("email"), "@", "").alias("domain")
)
B) customerDF.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1))
C) customerDF.select(
col("email").substr(0, 5).alias("username"),
col("email").substr(-5).alias("domain")
)
D) customerDF.withColumn("username", substring_index(col("email"), "@", 1)) \
.withColumn("domain", substring_index(col("email"), "@", -1))

3. 23 of 55.
A data scientist is working with a massive dataset that exceeds the memory capacity of a single machine. The data scientist is considering using Apache Spark™ instead of traditional single-machine languages like standard Python scripts.
Which two advantages does Apache Spark™ offer over a normal single-machine language in this scenario? (Choose 2 answers)

A) It can distribute data processing tasks across a cluster of machines, enabling horizontal scalability.
B) It eliminates the need to write any code, automatically handling all data processing.
C) It processes data solely on disk storage, reducing the need for memory resources.
D) It has built-in fault tolerance, allowing it to recover seamlessly from node failures during computation.
E) It requires specialized hardware to run, making it unsuitable for commodity hardware clusters.

4. 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) Support for SQL queries on structured data
C) Ability to process small datasets efficiently
D) In-memory computation and parallel processing capabilities

5. What is the benefit of Adaptive Query Execution (AQE)?

A) It automatically distributes tasks across nodes in the clusters and does not perform runtime adjustments to the query plan.
B) It allows Spark to optimize the query plan before execution but does not adapt during runtime.
C) It optimizes query execution by parallelizing tasks and does not adjust strategies based on runtime metrics like data skew.
D) It enables the adjustment of the query plan during runtime, handling skewed data, optimizing join strategies, and improving overall query performance.

Solutions: