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DAS-C01 · Question #116

DAS-C01 Question #116: Real Exam Question with Answer & Explanation

The correct answer is B: Configure the data analysis queue to enable concurrency scaling.. {"question_number": 5, "correct_answer": "B", "explanation": "Enabling concurrency scaling on the data analysis queue allows Amazon Redshift to automatically add transient read capacity when the queue experiences bursts, routing overflow queries to temporary additional cluster re

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Question

A company uses Amazon Redshift for its data warehousing needs. ETL jobs run every night to load data, apply business rules, and create aggregate tables for reporting. The company's data analysis, data science, and business intelligence teams use the data warehouse during regular business hours. The workload management is set to auto, and separate queues exist for each team with the priority set to NORMAL. Recently, a sudden spike of read queries from the data analysis team has occurred at least twice daily, and queries wait in line for cluster resources. The company needs a solution that enables the data analysis team to avoid query queuing without impacting latency and the query times of other teams. Which solution meets these requirements?

Options

  • AIncrease the query priority to HIGHEST for the data analysis queue.
  • BConfigure the data analysis queue to enable concurrency scaling.
  • CCreate a query monitoring rule to add more cluster capacity for the data analysis queue when
  • DUse workload management query queue hopping to route the query to the next matching queue.

Explanation

{"question_number": 5, "correct_answer": "B", "explanation": "Enabling concurrency scaling on the data analysis queue allows Amazon Redshift to automatically add transient read capacity when the queue experiences bursts, routing overflow queries to temporary additional cluster resources without affecting the main cluster's performance for other teams. This directly solves the queuing problem during spikes without degrading the data science or BI teams' query latency. Option A (HIGHEST priority) would give the data analysis team preferential access to the existing cluster resources, starving other teams and impacting their latency - explicitly prohibited by the requirement. Option C (query monitoring rules) can trigger actions like aborting or hopping queries but cannot dynamically add cluster capacity. Option D (queue hopping) moves queries to another queue but does not eliminate queuing or add capacity.", "generated_by": "claude-sonnet", "llm_judge_score": 4}

Topics

#Amazon Redshift#Workload Management (WLM)#Concurrency Scaling#Query Performance

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