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Exam Certified Data Engineer Associate topic 1 question 39 discussion

Actual exam question from Databricks's Certified Data Engineer Associate
Question #: 39
Topic #: 1
[All Certified Data Engineer Associate Questions]

A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team’s queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team’s queries?

  • A. They can increase the cluster size of the SQL endpoint.
  • B. They can increase the maximum bound of the SQL endpoint’s scaling range.
  • C. They can turn on the Auto Stop feature for the SQL endpoint.
  • D. They can turn on the Serverless feature for the SQL endpoint.
  • E. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to “Reliability Optimized.”
Show Suggested Answer Hide Answer
Suggested Answer: B 🗳️
by XiltroX at April 2, 2023, 7:24 p.m.

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damaldon
Highly Voted 1 year, 5 months ago
Answer is B. According to databricks documentation: -Sequentially -> Increase cluster size -Concurrent --> Scale out cluster
upvoted 30 times
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mokrani
Highly Voted 1 year, 3 months ago
Answer B is correct For those who's selected the same answer as the question 40 in the Databricks exam training, be careful becaue it's quite different: - Here the question is about simultaneously runs -> Scale Out clusters (involves adding more clusters) - In the Databricks exam training, the question is about "sequentially run queries" -> Scale Up (increasing the size of the nodes) Pleas refer to the this accepted answer https://community.databricks.com/t5/data-engineering/sequential-vs-concurrency-optimization-questions-from-query/td-p/36696
upvoted 16 times
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andie123
Most Recent 3 weeks ago
Selected Answer: A
When many users are running small queries simultaneously on a SQL warehouse (prior: endpoint), the database can become overloaded, causing slow query execution times. By increasing the cluster size of the SQL warehouse, the database can handle more simultaneous queries, resulting in faster query execution times. -> A
upvoted 1 times
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806e7d2
2 months, 2 weeks ago
Selected Answer: B
The issue described is related to query latency when multiple users are running queries simultaneously, all using the same SQL endpoint. This often leads to contention for resources, causing delays in query processing. To address this, the maximum scaling range of the SQL endpoint can be increased, which allows the endpoint to dynamically scale and handle more concurrent queries by adding more resources (e.g., additional nodes) as needed. In Databricks SQL, SQL endpoints can be scaled horizontally (adding more nodes) to better handle concurrency. By increasing the maximum scaling range, the endpoint will be able to scale more aggressively during periods of high load, improving query performance for concurrent users.
upvoted 1 times
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MohdAltaf19
4 months, 2 weeks ago
Correct Answers B Through put > Sequential > Scale Up Performance > Concurrent > Scale Out
upvoted 1 times
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7a22144
5 months, 3 weeks ago
B is correct because increasing the maximum bound of the SQL endpoint’s scaling range allows the endpoint to handle a larger number of queries by automatically scaling up the resources (e.g., adding more clusters). This approach addresses the issue of slow queries due to high concurrent usage, as more resources will become available to handle the increased load from simultaneous queries.
upvoted 2 times
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benni_ale
9 months, 1 week ago
Selected Answer: B
simultaneously probably means concurrently so scaling out the cluster is better
upvoted 1 times
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sakis213
10 months, 1 week ago
Selected Answer: B
B is correct
upvoted 1 times
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niharam2021
12 months ago
A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously4
upvoted 2 times
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agAshish
1 year ago
Answer is A , Q40 -- https://files.training.databricks.com/assessments/practice-exams/PracticeExam-DataEngineerAssociate.pdf
upvoted 3 times
6aa83ae
5 months ago
differenct question
upvoted 1 times
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K_yamini
12 months ago
the question on Practice set is slightly different if you look closely :-In the first scenario, the data analyst notes slow query performance for sequentially run queries on a SQL endpoint that isn't shared with other users. This suggests that the problem may be related to the configuration or performance of the SQL endpoint itself rather than contention with other users. In the second scenario, the data analysis team experiences slow query performance when multiple team members are running queries simultaneously on the same SQL endpoint. This indicates potential resource contention or limitations on the SQL endpoint when handling concurrent queries from multiple users. Given these differences, the approaches to address the issues may also differ:
upvoted 2 times
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Nika12
1 year ago
Selected Answer: B
Just got 100% on the exam. B was correct. Also, here is the link to good explanation: https://docs.databricks.com/en/compute/cluster-config-best-practices.html
upvoted 5 times
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Ody__
1 year ago
Selected Answer: A
A is correct
upvoted 1 times
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Ody__
1 year ago
Selected Answer: A
correct answer is A Question 40: https://files.training.databricks.com/assessments/practice-exams/PracticeExam-DataEngineerAssociate.pdf
upvoted 2 times
AdamNowak
1 month, 1 week ago
the question is about concurrent small queries this one in pdf is about sequential
upvoted 1 times
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CommanderBigMac
4 months, 3 weeks ago
Completely different question
upvoted 1 times
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SerGrey
1 year ago
Selected Answer: B
B is correct
upvoted 2 times
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nedlo
1 year, 1 month ago
Selected Answer: B
its B because its "simultanously by many users" so you have to scale it horizontally by increasing number of nodes : https://community.databricks.com/t5/data-engineering/sequential-vs-concurrency-optimization-questions-from-query/td-p/36696
upvoted 3 times
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pc1337xd
1 year, 2 months ago
Selected Answer: B
Issues occur when too many users are running queries at the same time -> Increase scaling so more clusters handle the queries
upvoted 5 times
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god_father
1 year, 3 months ago
Selected Answer: B
Increasing cluster size is for vertical scalability of query execution, while scaling out cluster is for horizontal scalability of query execution
upvoted 2 times
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