Explain the Advantages of Using Pandas API on Spark. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 2)

Questions 6–9

1. 6application · medium

   A financial analytics firm currently processes daily transaction reports using pandas. The reports have grown from 10 GB to 2 TB, and the firm's single server now takes over 12 hours to complete the job, exceeding the SLA. The team is proficient in pandas but has no experience with distributed systems. Which solution best meets the performance and skill-set constraints?

   • AOptimize the existing pandas code by using vectorized operations and avoiding loops.
   • BPurchase a larger server with more CPU cores and RAM to run the pandas job faster.
   • CMigrate the pandas scripts to the Pandas API on Spark, which can distribute the workload across a cluster while preserving the familiar syntax.
   • DSwitch to using Spark DataFrames with the SQL API, requiring the team to learn a new query language.
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2. 7application · medium

   A retail company has a legacy batch job written in pandas that calculates inventory forecasts. The job runs on a single server and now takes 8 hours, but the business needs results in under 1 hour. The company has an existing Spark cluster that is underutilized. The data engineering team wants to avoid a full rewrite and maintain the current code structure as much as possible. What should they do?

   • AIncrease the memory and CPU of the single server to speed up the pandas job.
   • BUse the pandas UDF API to distribute the existing code across the Spark cluster without any code changes.
   • CRun the pandas job on the Spark cluster using the Pandas API on Spark, which allows the existing code to be distributed with minimal changes.
   • DRewrite the job using PySpark RDDs for maximum performance.
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4. 8foundation · easy

   A team of data engineers is evaluating the Pandas API on Spark for a new project. Their primary goal is to reduce the learning curve for their data science team, which is already experienced with pandas. Which benefit of the Pandas API on Spark directly addresses this goal?

   • AIt provides a familiar, pandas-like syntax so that existing pandas users can be productive with minimal additional training.
   • BIt removes the need for the team to understand Spark concepts such as partitions, transformations, and actions.
   • CIt automatically optimizes all queries to be faster than equivalent pandas code running on a single machine.
   • DIt guarantees that any existing pandas script will run without modification on a Spark cluster.
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5. 9application · medium

   A marketing analytics team uses pandas for ad-hoc analysis on sample data. They now need to run the same analysis on the full 10 TB dataset stored in a data lake. The team is comfortable with pandas but has no experience with Spark SQL or RDDs. Which approach allows them to perform the analysis with the least amount of new learning?

   • AUse RDDs with Python lambdas to process the data.
   • BUse the Pandas API on Spark to run the analysis on the full dataset with a familiar DataFrame API.
   • CLoad the data into a pandas DataFrame on a single large-memory node.
   • DUse Spark SQL to query the data, learning the SQL dialect.
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