Utilize Common Data Sources Such as JDBC, Files, Etc., to Efficiently Read from and Write to Spark DataFrames Using Spark SQL, Including Overwriting and Partitioning by Column. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   Which Spark SQL option is required when reading from a JDBC source to identify the specific table or query to read?

   • Adbtable
   • Bfetchsize
   • CpartitionColumn
   • DnumPartitions
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2. 12foundation · easy

   Which Spark SQL statement writes a DataFrame to a directory of JSON files?

   • Adf.write.json("/path/output")
   • Bdf.write.format("csv").save("/path/output")
   • Cdf.write.format("json").save("/path/output")
   • Ddf.write.format("parquet").save("/path/output")
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4. 13foundation · easy

   Which Spark SQL method is used to write a DataFrame partitioned by one or more columns?

   • Arepartition
   • BpartitionBy
   • CbucketBy
   • DsortBy
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5. 14foundation · easy

   When writing a DataFrame partitioned by the 'year' column, how does Spark organize the output files?

   • AIt creates files named 2023.parquet, 2024.parquet, etc.
   • BIt creates subdirectories named year=2023, year=2024, etc.
   • CIt creates a single file with all data sorted by year.
   • DIt creates a single directory with all files, and the year is stored in a metadata file.
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6. 15expert · hard

   A team has a large dataset stored as CSV files. They frequently run queries that filter on a 'status' column and aggregate by a 'category' column. They want to improve query performance and reduce storage costs. They are considering converting to Parquet. Which additional step is most important to maximize the benefit?

   • AUse the compression option to compress the Parquet files.
   • BIncrease the number of output files to improve parallelism.
   • CPartition the Parquet data by the 'status' column.
   • DSort the Parquet data by the 'category' column.
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