Save Data to Persistent Tables While Applying Sorting and Partitioning to Optimize Data Retrieval. ASSOCIATE-DEVELOPER-APACHE-SPARK Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A data analyst wants to save a DataFrame as a table that can be queried in future Spark sessions. They also want to ensure that the table is stored in a specific directory on the company's data lake. Which approach should they use?

   • Adf.write.option("path", "/data/lake/table").saveAsTable("analytics_table")
   • Bdf.createOrReplaceTempView("analytics_table")
   • Cdf.write.mode("overwrite").saveAsTable("analytics_table")
   • Ddf.write.saveAsTable("analytics_table")
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2. 2expert · hard

   A company has a table with columns `country`, `city`, and `user_id`. Queries frequently filter on `country` and `city`, and also join on `user_id`. The table is currently partitioned by `country`. You need to improve performance for both the filter and the join. Which strategy is most effective?

   • AReplace partitioning by `country` with partitioning by `user_id`
   • BSort the table by `user_id` within each partition
   • CAdd `city` as a second partition column and bucket by `user_id`
   • DBucket by `country` and `city`
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4. 3foundation · easy

   What is the primary benefit of partitioning a table by a column like 'date'?

   • AIt ensures that data is sorted within each partition.
   • BIt allows queries to skip entire partitions that do not match the filter, reducing data scanned.
   • CIt reduces the amount of data shuffled during joins.
   • DIt improves performance for all queries regardless of the filter.
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5. 4expert · hard

   A media streaming company stores user viewing history in a large table. Queries frequently filter on `user_id` to retrieve a user's viewing history, and also join the table with a `users` table on `user_id`. The table is currently partitioned by `country` and `date`. Query performance is poor. You need to optimize for both the filter and the join. Which strategy should you choose?

   • AReplace partitioning by `country` and `date` with partitioning by `user_id`
   • BSort the table by `user_id` without bucketing or partitioning
   • CBucket the table by `user_id` into 64 buckets and keep the existing partitioning by `country` and `date`
   • DRemove partitioning and bucket by `country` and `date`
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6. 5foundation · easy

   When is bucketing most beneficial for query performance?

   • AWhen performing frequent joins on the bucketing column.
   • BWhen filtering on a column with high cardinality.
   • CWhen the table is small and fits in memory.
   • DWhen the table is written once and never updated.
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