Identify the Features of the Apache Spark Modules, Including Core, Spark SQL, DataFrames, Pandas API on Spark, Structured Streaming, and MLib. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A company needs to ingest real-time clickstream events from a message broker, perform windowed aggregations (e.g., count clicks per minute), and write the results to a sink. The solution must be fault-tolerant and provide exactly-once semantics. Which Spark module should they use?

   • ASpark Core, by implementing a custom streaming solution using RDDs and a scheduler.
   • BStructured Streaming, which provides a scalable and fault-tolerant stream processing engine built on the Spark SQL engine.
   • CMLlib, which provides streaming machine learning algorithms.
   • DThe Pandas API on Spark, which can process streaming data with a pandas-like interface.
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2. 2application · medium

   A team is building a recommendation system. They have a large dataset of user-item interactions and need to train a collaborative filtering model. They also need to evaluate the model's performance using metrics like RMSE. Which Spark module provides these capabilities?

   • AMLlib, which provides collaborative filtering algorithms and evaluation metrics.
   • BStructured Streaming, which can train models on streaming data.
   • CThe Pandas API on Spark, which provides a pandas-like interface for machine learning.
   • DSpark SQL, which can be used to compute RMSE with SQL queries.
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4. 3application · medium

   A team has a complex data transformation pipeline written using the DataFrame API. They want to improve its performance by leveraging Spark's query optimizer. They have heard that Spark can optimize the execution plan. Which module is responsible for this optimization?

   • AMLlib, which provides optimized implementations of machine learning algorithms.
   • BStructured Streaming, which optimizes the streaming queries.
   • CSpark SQL, which includes the Catalyst optimizer to improve query performance.
   • DSpark Core, which provides the execution engine that runs the optimized plan.
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5. 4application · medium

   A data scientist has a Python script that heavily uses pandas for data wrangling, including custom functions applied to columns. The script works on a sample of the data but takes too long on the full dataset. They want to run the same script on the full dataset using Spark. What is the most efficient way to achieve this?

   • ARewrite the script to use RDDs and map functions.
   • BUse MLlib to parallelize the pandas operations.
   • CUse the Pandas API on Spark to run the script with minimal changes.
   • DConvert the script to SQL queries and run them with Spark SQL.
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6. 5foundation · easy

   Which of the following is a fundamental data abstraction provided by Spark Core that represents an immutable, distributed collection of elements that can be operated on in parallel?

   • ARDD (Resilient Distributed Dataset)
   • BStreamingContext
   • CDataFrame
   • DDataset
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