Apache-Spark-Developer Sample Questions

Apache-Spark-Developer Sample Questions & Answers

Building DataFrame and DataSet API applications, from manipulations to aggregations, is the single biggest topic, alongside tables and data sources in Spark SQL, core Spark architecture and execution patterns, the Pandas API on Spark, and structured streaming.

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  1. Question 1Advanced

    Developing Apache Spark DataFrame/DataSet API Applications · Combine DataFrames with operations such as Inner join, left join, broadcast join, multiple keys, cross join, union, union all

    A retail analytics company is building a daily batch processing pipeline using Spark. The pipeline must ingest raw sales transaction data from a CSV file, enrich it with product dimension data from a Parquet file, calculate daily sales aggregates per product category, and write the final report to a Delta table, overwriting the previous day's report.

    The raw sales data (sales_df) contains product_id, sale_amount, and transaction_time. The product dimension data (products_df) contains product_id and product_category. The final report must be partitioned by product_category for efficient querying by downstream business intelligence tools.

    Which sequence of PySpark DataFrame operations correctly and most efficiently implements this logic?

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    Correct answer: B

    This is the correct and most efficient sequence. First, the sales data is joined with the product data to add the product_category. A left outer join is appropriate to ensure no sales are dropped if a product is missing from the dimension table. Second, the enriched data is grouped by the product_category to calculate the sum of sales. Performing the join before aggregation is crucial to have the category available. Finally, the aggregated result is written to a Delta table, correctly using overwrite mode and partitioning by product_category for query performance.

  2. Question 2Beginner

    Using Spark Connect to deploy applications · Describe the features of Spark Connect

    True or False: Spark Connect allows a Spark application's driver process to run on a separate machine from the Spark cluster, such as a developer's laptop or an IDE, while the execution of Spark jobs occurs on the remote cluster.

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    Correct answer: A

    This statement is true. The primary purpose of Spark Connect is to decouple the client application (where the SparkSession is created and DataFrame logic is defined) from the Spark driver and cluster. It introduces a client-server architecture where the client sends unresolved logical plans to a Spark Connect server running on the cluster, which then translates them into Spark's physical plan for execution on the executors.

  3. Question 3Intermediate

    Using Pandas API on Apache Spark · Create and invoke Pandas UDF

    A data scientist is working with a large Spark DataFrame and needs to apply a complex numerical computation that is already implemented and highly optimized in the scipy library. Which type of User-Defined Function is best suited for applying this scipy function to columns of a Spark DataFrame to maximize performance?

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    Correct answer: C

    A Pandas UDF (also known as a vectorized UDF) is the best choice. It processes data in batches as pandas Series or DataFrames, which allows for efficient, vectorized computations using libraries like NumPy or SciPy. This avoids the high overhead of serialization/deserialization and row-by-row processing that occurs with standard Python UDFs, leading to significant performance improvements.

  4. Question 4Beginner

    Apache Spark Architecture and Components · Identify the role of core components of Apache Spark's Architecture including cluster, driver node, worker nodes/executors, CPU cores, memory

    Which of the following describes the role of the Driver in a Spark application running in cluster mode?

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    Correct answer: B

    In cluster mode, the driver program is launched on a worker node within the cluster. Its main responsibilities are to host the main() method of the application, create the SparkSession, analyze and schedule jobs, and coordinate the execution of tasks on the executors. The executors are the processes that actually run the computation tasks.

  5. Question 5Beginner

    Using Spark SQL · Utilize common data sources such as JDBC, files, etc. to efficiently read from and write to Spark DataFrames using SparkSQL, including overwriting and partitioning by column

    A developer is writing a DataFrame to a cloud storage location. The requirement is to write the data only if the target location does not already exist. If the location exists, the write operation should fail instead of overwriting or appending data. Which saveMode should be used?

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    Correct answer: D

    SaveMode.ErrorIfExists is the correct option. It is the default behavior. If the target location already exists, a AnalysisException is thrown. Append adds data, Overwrite replaces it, and Ignore silently does nothing if the location exists.

  6. Question 6Intermediate

    Developing Apache Spark DataFrame/DataSet API Applications · Describe the purpose and implementation of broadcast joins

    A developer is joining a very large fact table (events_df) with a small dimension table (users_df). To optimize the join performance, the developer decides to use a broadcast join. Which code snippet correctly hints to Spark to perform a broadcast join?

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    Correct answer: A

    The broadcast() function from pyspark.sql.functions is used to explicitly hint to the Spark optimizer that the provided DataFrame (in this case, the small users_df) should be broadcasted to all executors. This avoids a costly shuffle of the large events_df. The other options use incorrect syntax or functions.

  7. Question 7Intermediate

    Troubleshooting and Tuning Apache Spark DataFrame API Applications · Implement performance tuning strategies & optimize cluster utilization including partitioning, repartitioning, coalescing

    What is the primary difference between the repartition() and coalesce() transformations on a DataFrame?

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    Correct answer: B

    The key difference lies in the use of a shuffle. repartition() can be used to increase or decrease the number of partitions and always performs a full shuffle, which is expensive but results in evenly sized partitions. coalesce() is an optimized version used only to decrease the number of partitions. It avoids a full shuffle by combining existing partitions on the same worker node, making it more efficient for reducing partition count but potentially leading to unevenly sized partitions.

  8. Question 8Advanced

    Structured Streaming · Perform Streaming Deduplication in Structured Streaming, both with and without watermark usage

    A streaming application processes IoT sensor data that may arrive late. To handle this, a watermark of 10 minutes is defined on the event timestamp column. The application performs a windowed aggregation with a 5-minute tumbling window. What is the role of the watermark in this scenario?

    graph TD subgraph Stream Processing Input[Input Stream] -->|Events with Timestamps| Watermark{Define Watermark \n(10 minutes)} Watermark --> Window{Window Aggregation \n(5-minute windows)} Window --> StateStore[(State Store for Windows)] Window --> Output[Output Sink] end LateEvent[Late Event \n(>10 min late)] -.->|Dropped| Watermark
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    Correct answer: B

    A watermark allows Spark's Structured Streaming engine to manage the state for windowed aggregations. It defines a threshold for how late data can be. The engine tracks the maximum event time seen so far and will only maintain state for windows whose end time is greater than (max_event_time - watermark_delay). Any data arriving for a window older than this threshold is considered too late and is dropped, allowing Spark to safely evict old state from memory and prevent it from growing indefinitely.

  9. Question 9Beginner

    Using Spark SQL · Register DataFrames as temporary views in Spark SQL, allowing them to be queried with SQL syntax.

    A developer needs to create a temporary table-like entity from a DataFrame that can be queried using Spark SQL, but only within the current SparkSession. Which method should be used?

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    Correct answer: B

    createOrReplaceTempView() creates a temporary view that is scoped to the SparkSession in which it was created. This view is not persisted to the metastore and will be dropped when the session terminates. saveAsTable creates a persistent table. createGlobalTempView creates a view visible across all sessions, and createTempTable is a deprecated method.

  10. Question 10Intermediate

    Developing Apache Spark DataFrame/DataSet API Applications · Manipulate columns, rows, and table structures by adding, dropping, splitting, renaming column names, applying filters, and exploding arrays

    A DataFrame has a column named attributes which is a string containing key-value pairs in a JSON format (e.g., '{"color":"blue","size":"large"}'). The developer needs to extract the value associated with the color key into a new column named product_color. Which code snippet accomplishes this?

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    Correct answer: C

    The get_json_object function is specifically designed to extract JSON elements from a JSON string column using a JSON path expression. The path $.color correctly selects the value of the 'color' key from the root of the JSON object. from_json is used to parse an entire JSON string into a struct type, which is more than what is needed here. col("attributes.color") only works if the column is already a struct type, not a string.

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