Databricks Certified Associate Developer for Apache Spark

The Databricks Certified Associate Developer for Apache Spark validates a deep understanding of the Spark DataFrame API and its application in data processing. It focuses on the fundamental architecture of Spark and the ability to perform data manipulation tasks using Python or Scala. Professionals with the symbol DTB_ADAS are recognized for their skills in building efficient big data processing applications.

---------- Question 1

A Spark application performs several iterative operations on a large DataFrame. To improve performance and prevent recomputing the DataFrame multiple times, which operation should be applied?

1. repartition()
2. persist()
3. collect()
4. coalesce()

---------- Question 2

A data engineer needs to load a large CSV file, apply a filter, and then save the results as a Parquet file, overwriting existing data if present. Which Spark SQL save mode is most appropriate for the final write operation to achieve this overwriting behavior?

1. Append
2. ErrorIfExists
3. Overwrite
4. Ignore

---------- Question 3

A Spark job needs to write an updated DataFrame to an existing directory in HDFS. If the directory already contains data, the job should fail to prevent accidental data loss. Which Spark DataFrame saveMode should be used for this requirement?

1. overwrite
2. append
3. ignore
4. errorIfExists

---------- Question 4

A Spark job is consistently slow due to excessive data movement across the network. Investigation reveals that a DataFrame has many small partitions, leading to high overhead in task scheduling and execution. What DataFrame API operation is most suitable for reducing the number of partitions to improve performance in this scenario?

1. repartition
2. coalesce
3. sort
4. join

---------- Question 5

A streaming application processes customer clickstream data and needs to count unique clicks within specific time windows. To ensure correctness, how can duplicate events within a streaming DataFrame be handled using Structured Streaming features?

1. By applying a batch dropDuplicates transformation.
2. By using withWatermark and dropDuplicates on event time and an ID.
3. By configuring the source to ignore duplicates.
4. By manually managing a state store for seen events.

---------- Question 6

A large DataFrame (millions of rows) needs to be joined with a much smaller DataFrame (thousands of rows). To optimize performance and minimize data shuffling across the cluster, what type of join strategy is most suitable for this scenario?

1. Sort-Merge Join
2. Shuffle Hash Join
3. Broadcast Hash Join
4. Cartesian Join

---------- Question 7

A data scientist familiar with Pandas wants to scale their existing data analysis workflows to large datasets using Apache Spark without rewriting extensive code. What is the main advantage of using the Pandas API on Apache Spark in this scenario?

1. It automatically converts all Pandas operations into highly optimized Spark SQL queries.
2. It allows Pandas users to leverage Spark's distributed processing capabilities with minimal code changes.
3. It removes the need for a SparkSession, simplifying the application setup process entirely.
4. It provides better performance than native PySpark DataFrames for all types of operations.

---------- Question 8

In Structured Streaming, what mechanism ensures exactly-once fault tolerance for stateful operations like aggregations with a watermark?

1. Spark automatically retries all failed tasks until they succeed, guaranteeing completion.
2. It relies on the underlying distributed file system to store all intermediate states reliably.
3. Structured Streaming uses a combination of write-ahead logs and checkpointing to store state and progress information.
4. Each micro-batch is processed entirely in memory, and results are only committed upon successful completion.

---------- Question 9

During the execution of a Spark job, a developer observes an OutOfMemoryError OOM on an executor. Which action is generally the most effective first step to diagnose the root cause of this error?

1. Increase the driver memory immediately.
2. Decrease the number of executor cores.
3. Examine the Spark UI and executor logs for stack traces and memory usage.
4. Switch to a smaller dataset.

---------- Question 10

When joining a very large DataFrame with a significantly smaller DataFrame, what is a key advantage of using a broadcast join in Spark?

1. It forces a shuffle of both DataFrames, guaranteeing even data distribution.
2. It saves the larger DataFrame to disk entirely before joining.
3. It sends the smaller DataFrame to all executor nodes, avoiding a shuffle for the larger DataFrame.
4. It converts both DataFrames into RDDs before performing the join.

---

Are they useful?

Click here to get 270 more questions to pass this certification at the first try! Explanation for each answer is included!

Follow the below LINKEDIN channel to stay updated about 89+ exams!

Data Engineering Guild