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Exam AZ-301 topic 17 question 50 discussion

Actual exam question from Microsoft's AZ-301
Question #: 50
Topic #: 17
[All AZ-301 Questions]

You have an Azure SQL database named DB1 that contains multiple tables.
You need to improve the performance of DB1. The solution must minimize administrative effort.
What should you use?

  • A. Azure Monitor
  • B. Azure Advisor
  • C. Query Performance Insight
  • D. automatic tuning
Show Suggested Answer Hide Answer
Suggested Answer: D 🗳️
Azure SQL Database and Azure SQL Managed Instance automatic tuning provides peak performance and stable workloads through continuous performance tuning based on AI and machine learning.
Automatic tuning is a fully managed intelligent performance service that uses built-in intelligence to continuously monitor queries executed on a database, and it automatically improves their performance.
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/automatic-tuning-overview
by [deleted] at Aug. 10, 2020, 12:20 p.m.

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Highly Voted 4 years, 10 months ago
A. Azure Monitor > Does not automatically improve DB performance by itself. It would give some insights but still would require manual intervention. B. Azure Advisor > Does not generate any DB performance recommendation from what I know. Please correct me if I am wrong. C. Query Performance Insight > Would help to improve performance by identifying long-running queries. However manual adjustment would still be necessary. D. automatic tuning > Provides continuous performance tuning based on AI and machine learning and allows for automatic optimizations. Conclusion: D is the best answer
upvoted 20 times
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smsulai
Most Recent 4 years, 10 months ago
If we need to improve prformance of query then we can use Query Performance insight to identify and improve long running queris but the question is to improve the performance of db hence given answer is correct as this can be achieved by different ways like AI, ML etc
upvoted 1 times
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Shunya
4 years, 10 months ago
Correct answer
upvoted 2 times
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Community vote distribution
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C (25%)
B (20%)
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