Execute SQL Queries Directly on Files, Including ORC Files, JSON Files, CSV Files, Text Files, and Delta Files, and Understand the Different Save Modes for Outputting Data in Spark SQL. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   Which statement best describes how Spark SQL can query data stored in files such as Parquet, JSON, or CSV?

   • ASpark SQL requires all file-based queries to use an external catalog such as AWS Glue or Unity Catalog.
   • BSpark SQL can query files directly by referencing the file path in the FROM clause, without requiring a separate metastore.
   • CSpark SQL can only query files that have been previously registered as temporary views in the same session.
   • DSpark SQL requires a Hive metastore to be configured before any file-based query can be executed.
   Reveal Solution Discussion Select an answer first
2. 7foundation · easy

   When querying a CSV file directly with Spark SQL, what is the default behavior regarding the header row?

   • ASpark requires you to specify the header option in every query.
   • BSpark automatically uses the first row as column names.
   • CSpark treats the first row as data, not as column names.
   • DSpark ignores the header row and generates column names like `_c0`, `_c1`.
   Reveal Solution Discussion Select an answer first
4. 8foundation · easy

   Which option would you use when querying a CSV file to specify a custom delimiter such as a semicolon?

   • A`delimiter` option set to `;`
   • B`separator` option set to `;`
   • C`quote` option set to `;`
   • D`sep` option set to `;`
   Reveal Solution Discussion Select an answer first
5. 9application · medium

   A streaming job writes JSON data to /data/events. A separate batch job reads this data and writes aggregated results to /data/aggregates. The batch job runs every hour and should only write if the aggregates file does not already exist. If it exists, the job should skip writing without failing. Which save mode should the batch job use?

   • AAppend
   • BErrorIfExists
   • CIgnore
   • DOverwrite
   Reveal Solution Discussion Select an answer first
6. 10application · medium

   A JSON file at /data/users.json has inconsistent schemas across lines. Some lines have a 'phone' field, others do not. An analyst wants to query the 'phone' field, but the schema inference fails. What is the most effective way to handle this?

   • AUse the json.`/data/users.json` syntax and let Spark infer the schema, then query the 'phone' field.
   • BQuery the file with a WHERE clause to filter lines that have the 'phone' field.
   • CPreprocess the file to add the 'phone' field to all lines before querying.
   • DProvide an explicit schema using the schema option in a DataFrame reader, then register a temporary view.
   Reveal Solution Discussion Select an answer first