MLA-C01 · Question #234
A company develops a recommendation model and hosts the model on an Amazon SageMaker AI endpoint. The model uses the SageMaker AI endpoint to perform near real-time inference to deliver personalized…
The correct answer is A. Use SageMaker Clarify to analyze changes in feature distribution. Configure SageMaker Model. The company needs a proactive solution that both detects distribution shifts in input features and monitors model performance before future campaigns. Option A combines two complementary tools: SageMaker Clarify analyzes changes in feature distribution (detecting data drift…
Question
A company develops a recommendation model and hosts the model on an Amazon SageMaker AI endpoint. The model uses the SageMaker AI endpoint to perform near real-time inference to deliver personalized product recommendations to customers based on browsing history, purchase records, and in-app user interactions. After a major marketing campaign, the company observes a sharp drop in the model’s performance. The company needs a solution to proactively monitor, detect, and validate model performance before future marketing campaigns. Which solution will meet these requirements?
Options
- AUse SageMaker Clarify to analyze changes in feature distribution. Configure SageMaker Model
- BUse Amazon CloudWatch dashboards to monitor endpoint metrics. Use SageMaker Model
- CUse SageMaker Clarify for bias detection. Set up Amazon CloudWatch alarms to monitor model
- DUse SageMaker Model Monitor to monitor constraints. Use Amazon CloudWatch Logs Insights to
How the community answered
(24 responses)- A83% (20)
- B8% (2)
- C4% (1)
- D4% (1)
Explanation
The company needs a proactive solution that both detects distribution shifts in input features and monitors model performance before future campaigns. Option A combines two complementary tools: SageMaker Clarify analyzes changes in feature distribution (detecting data drift that signals when model inputs are shifting), and SageMaker Model Monitor enforces statistical constraints and triggers alerts when thresholds are breached - enabling proactive validation before a campaign launches. Option B uses CloudWatch dashboards reactively and lacks the feature-distribution analysis layer. Option C limits Clarify to bias detection, missing the broader data drift analysis needed. Option D uses Model Monitor for constraints but relies on CloudWatch Logs Insights for detection, which is a reactive log query tool rather than a proactive drift detection mechanism.
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