CERTIFIED-MACHINE-LEARNING-PROFESSIONAL · Question #4
CERTIFIED-MACHINE-LEARNING-PROFESSIONAL Question #4: Real Exam Question with Answer & Explanation
The correct answer is D: Because the streaming deployment is always on, there is a need to confirm that the deployment. Option D is correct because streaming deployments run continuously and autonomously - unlike batch jobs that are manually triggered and monitored, a streaming system processes data 24/7 without a practitioner reviewing each run. Rigorous data type checks are therefore essential t
Question
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?
Options
- ABecause the streaming deployment is always on, all types of data must be handled without
- BAll of these statements
- CBecause the streaming deployment is always on, there is no practitioner to debug poor model
- DBecause the streaming deployment is always on, there is a need to confirm that the deployment
- ENone of these statements
Explanation
Option D is correct because streaming deployments run continuously and autonomously - unlike batch jobs that are manually triggered and monitored, a streaming system processes data 24/7 without a practitioner reviewing each run. Rigorous data type checks are therefore essential to ensure the deployment can handle format changes or unexpected inputs without silently failing or crashing mid-stream.
Why the distractors are wrong:
- Option A is incorrect because the goal is not to handle "all types" of data indiscriminately - it's to validate that expected data formats are correct, not to blindly accept everything.
- Option C is partially related (lack of a practitioner is real), but it conflates model performance debugging with data format validation - these are distinct concerns, and C misidentifies the primary reason for data type tests specifically.
- Option B ("All of these") fails because A and C are not accurate characterizations of why data type tests matter, making the umbrella answer invalid.
- Option E ("None") is clearly wrong since D correctly captures the core motivation.
Memory tip: Think "Always-On = Always Validate First." In batch deployments, a human re-runs the job if something breaks. In streaming, bad data silently corrupts outputs for hours - so the system must be its own gatekeeper through automated data type checks.
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