Task 2.4: Design Data Models and Schema Evolution DEA-C01 Practice Questions (Page 6)

Questions 26–30

1. 26foundation · easy

   A data engineer notices that a source system has started emitting JSON files instead of the previous CSV format. Which data characteristic change does this represent?

   • AVolume
   • BVelocity
   • CVeracity
   • DVariety
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2. 27foundation · easy

   In AWS Lake Formation, which component represents a logical container that holds tables and is used to organize data in the data lake?

   • AData location
   • BDatabase
   • CData catalog
   • DTable
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4. 28expert · hard

   A company uses AWS Lake Formation to manage a data lake. A new compliance requirement mandates that the data lake must be able to track which users and groups have accessed specific tables and columns. The security team needs to generate a report of all access attempts to a sensitive table. What is the most effective way to meet this requirement?

   • AUse Amazon GuardDuty to monitor for suspicious access patterns.
   • BUse AWS Config rules to monitor changes to the Lake Formation permissions.
   • CEnable AWS CloudTrail data events for the Lake Formation APIs and query the logs for access attempts.
   • DEnable S3 server access logs on the data lake bucket and analyze them for access patterns.
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5. 29foundation · easy

   In an AWS DMS migration task, what is the role of the 'schema conversion' feature?

   • AIt validates data quality after migration.
   • BIt converts the source schema to a format compatible with the target database engine.
   • CIt creates an S3 bucket for staging data.
   • DIt compresses the data before transfer.
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6. 30application · medium

   A media streaming company stores user clickstream logs in an Amazon S3 data lake. The data is partitioned by year, month, and day. The volume of data has grown significantly, and now daily partitions contain hundreds of millions of records. Analysts frequently run queries that filter for specific user_id values within a single day. The current partitioning strategy is causing queries to scan entire daily partitions, resulting in high costs and slow performance. Which partitioning strategy change would most directly improve query performance for this access pattern?

   • AKeep the current partitioning but enable S3 Inventory to track the files.
   • BConvert the data to a columnar format like Parquet and keep the existing partition structure.
   • CChange the partition structure to year/month/day/hour to increase the number of partitions.
   • DAdd user_id as an additional partition level, creating a structure like year/month/day/user_id.
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