A team has an external Hive table pointing to HDFS data that is also processed by other applications. They accidentally run DROP TABLE on the external table. What is the result?

The table metadata is removed, but the HDFS data files remain intact.

The table becomes inaccessible until the data is reloaded.

The HDFS data files are deleted, but the table metadata remains.

The table metadata and the HDFS data files are both deleted.

A company needs to support both interactive dashboards with sub-second latency and large batch analytics on the same dataset. They are considering using Hive for both workloads. Which approach is most appropriate?

Use Hive for both, relying on Tez and vectorization to achieve sub-second latency.

Use Hive for batch analytics and a separate interactive query engine (e.g., Presto or Impala) for dashboards.

Use Hive with LLAP for both workloads.

Use a traditional relational database for batch analytics and Hive for dashboards.

What is the primary purpose of partitioning in Hive tables?

To compress data to save storage space.

To enforce data integrity constraints like primary keys.

To improve query performance by reducing the amount of data scanned.

A Hive query that joins two large tables and aggregates results is running slowly. The execution engine is currently MapReduce. The team wants to improve performance without rewriting the query. Which configuration change should they make first?

Disable vectorization to reduce CPU usage.

Increase the number of mappers by setting mapred.map.tasks.

Convert the join to a map-side join by adding a hint.

Switch the execution engine to Tez.

A Hive table `events` is partitioned by `event_date` and bucketed by `user_id` into 16 buckets. Queries frequently filter on `event_date` and `user_id`. The team notices that queries filtering on both columns still scan all buckets. Which configuration or design change would most directly address this issue?

Ensure the query uses a bucketing predicate and enable hive.optimize.bucketpruning

Enable hive.optimize.bucketmapjoin

Use the CLUSTERED BY clause with SORTED BY on user_id

Set hive.optimize.bucketmapjoin.sortedmerge to true

Dell Data Science Optimize

Domain 2Objective 2

Hive DATA-SCIENCE-OPTIMIZE Practice Questions (Page 1)

Part of the Hadoop Ecosystem and NoSQL domain, which accounts for 15% of the DATA-SCIENCE-OPTIMIZE exam.

31questions here

7free pages

8concepts

15%of the exam

Questions 1–5

1expert · medium
A team has an external Hive table pointing to HDFS data that is also processed by other applications. They accidentally run DROP TABLE on the external table. What is the result?
Select an answer first
2expert · hard
A company needs to support both interactive dashboards with sub-second latency and large batch analytics on the same dataset. They are considering using Hive for both workloads. Which approach is most appropriate?
Select an answer first
3foundation · easy
What is the primary purpose of partitioning in Hive tables?
Select an answer first
4application · medium
A Hive query that joins two large tables and aggregates results is running slowly. The execution engine is currently MapReduce. The team wants to improve performance without rewriting the query. Which configuration change should they make first?
Select an answer first
5expert · hard
A Hive table `events` is partitioned by `event_date` and bucketed by `user_id` into 16 buckets. Queries frequently filter on `event_date` and `user_id`. The team notices that queries filtering on both columns still scan all buckets. Which configuration or design change would most directly address this issue?
Select an answer first

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