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Exam Certified Machine Learning Professional All Questions

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Exam Certified Machine Learning Professional topic 1 question 23 discussion

Actual exam question from Databricks's Certified Machine Learning Professional
Question #: 23
Topic #: 1
[All Certified Machine Learning Professional Questions]

A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.
Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?

  • A. Because the streaming deployment is always on, all types of data must be handled without producing an error
  • B. All of these statements
  • C. Because the streaming deployment is always on, there is no practitioner to debug poor model performance
  • D. Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
  • E. None of these statements
Show Suggested Answer Hide Answer
Suggested Answer: D 🗳️

Comments

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PincoPallinoQualunque
2 months, 2 weeks ago
All of these statements is not logically possible. Otherwise it would include also "non of these statements".
upvoted 1 times
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james_donquixote
6 months ago
Selected Answer: B
Agree with Bokninja
upvoted 1 times
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ThoBustos
6 months ago
Selected Answer: B
agree with BokNinja
upvoted 1 times
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spaceexplorer
9 months, 3 weeks ago
Selected Answer: A
A is correct
upvoted 1 times
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BokNinja
11 months, 1 week ago
The correct answer is B. All of these statements. In a streaming deployment, the system is always on and continuously processing data. Therefore, it’s crucial to handle all types of data without producing an error. Rigorous data tests and checks can help ensure that the system can handle changes in data formats and continue to operate smoothly. Additionally, because the system is always on, there may not be a practitioner available to debug poor model performance in real-time. Data tests and checks can help catch potential issues before they impact model performance. Finally, because the system is always on, it’s important to confirm that the deployment can autoscale to handle varying data volumes. Data tests and checks can help validate the system’s ability to scale.
upvoted 3 times
ThoBustos
6 months, 3 weeks ago
makes more sense to me
upvoted 1 times
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C (25%)
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