3.3 Using a Data Lake PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 3)

Questions 11–15

1. 11application · medium

   A gaming company stores player event logs in a data lake on Cloud Storage. The data is used for analytics, but the volume is growing rapidly, and the cost is increasing. The company wants to reduce costs without losing the ability to query recent data. The data is accessed less frequently after 30 days. What should the data engineer do?

   • AMove objects older than 30 days to a different bucket with a lower storage class.
   • BApply a lifecycle policy to change the storage class of objects older than 30 days to Coldline, and keep them in the same bucket.
   • CDelete objects older than 30 days to reduce storage costs.
   • DUse BigQuery to query the data directly from Cloud Storage without changing the storage class.
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2. 12expert · medium

   A data lake on Cloud Storage is used by a team of data analysts. The analysts frequently run queries that scan large amounts of data, and the cost of BigQuery queries is high. The company wants to reduce query costs without significantly impacting performance. The data is stored in Parquet files partitioned by date. What should the data engineer do?

   • AUse Cloud Dataflow to pre-aggregate the data and store the results in a separate bucket.
   • BUse BigQuery external tables with partition filters to limit the data scanned.
   • CLoad the data into BigQuery as a partitioned table and use clustering.
   • DUse Cloud Monitoring to track query costs and set up alerts.
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4. 13application · medium

   A logistics company ingests IoT sensor data from delivery vehicles into a data lake on Cloud Storage. The data arrives in JSON format with nested fields. Analysts need to run SQL queries on this data, but they also need to join it with reference data stored in a BigQuery table. The data volume is moderate and the analysts query the same data frequently. What should the data engineer do to enable efficient SQL querying?

   • ALoad the JSON data into a BigQuery native table using a load job and then join with the reference table.
   • BConvert the JSON to Avro and create a BigQuery external table, then join with the reference table.
   • CCreate a BigQuery external table over the JSON files and use federated queries to join with the reference table.
   • DUse Dataproc to run Spark SQL directly on the JSON files and write results to a separate bucket.
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5. 14expert · medium

   A data lake on Cloud Storage is used by multiple teams. The operations team notices that storage costs are increasing unexpectedly. They suspect that some teams are storing large amounts of temporary data that is not being cleaned up. The company wants to identify the source of the cost increase and implement a solution to prevent future waste. What should the data engineer do?

   • AUse Cloud Monitoring to track storage usage by bucket and set up alerts on unusual growth.
   • BEnable object lifecycle management to delete objects older than a certain age.
   • CImplement a tagging strategy and enforce a policy that requires all objects to have a lifecycle rule.
   • DUse Cloud Billing reports to analyze costs by bucket and set up budget alerts.
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6. 15expert · medium

   A data lake on Cloud Storage is experiencing performance issues. Queries that read from the lake are slow, and the operations team suspects that the data is not optimally organized. The data is stored in many small files, and the team wants to improve query performance and reduce costs. What should the data engineer do?

   • AUse a Cloud Dataflow job to compact small files into larger files.
   • BUse BigQuery external tables with wildcard URIs to read the small files.
   • CUse Cloud Storage lifecycle rules to delete small files.
   • DUse Cloud Monitoring to identify the slow queries and optimize them.
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