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NEW QUESTION # 37
A company has multiple models that are hosted on Amazon SageMaker Al. The models need to be re-trained.
The requirements for each model are different, so the company needs to choose different deployment strategies to transfer all requests to a new model.
Select the correct strategy from the following list for each requirement. Select each strategy one time. (Select THREE.)
. Canary traffic shifting
. Linear traffic shifting guardrail
. All at once traffic shifting
Answer:
Explanation:
Explanation:
1## Simultaneous calls to the endpoint must reach models with the same configuration Correct strategy: All at once traffic shifting Why:
"All at once" replaces the old model with the new model immediately. After the switch, all concurrent requests hit only the new model configuration, guaranteeing configuration consistency across simultaneous calls.
2## The new model must receive only a fraction of the requests for validation before receiving all the traffic Correct strategy: Canary traffic shifting Why:
Canary deployments route a small percentage of traffic (for example, 5% or 10%) to the new model first. This allows validation of correctness and performance before shifting 100% of traffic.
3## Traffic to the new model must increase gradually to ensure that pipelines that rely on the endpoint do not fail because of changes in latency Correct strategy: Linear traffic shifting guardrail Why:
Linear traffic shifting gradually increases traffic in equal increments over time and includes guardrails (such as CloudWatch alarms) to automatically roll back if latency or errors exceed thresholds.
NEW QUESTION # 38
A company is running ML models on premises by using custom Python scripts and proprietary datasets. The company is using PyTorch. The model building requires unique domain knowledge. The company needs to move the models to AWS.
Which solution will meet these requirements with the LEAST development effort?
Answer: A
Explanation:
The company already has custom Python training scripts, proprietary datasets, and uses PyTorch, with significant domain-specific logic embedded in the model. The goal is to migrate these workloads to AWS with the least development effort.
According to AWS documentation, Amazon SageMaker AI script mode is explicitly designed for this scenario. Script mode allows customers to bring their existing training scripts with minimal or no code changes and run them using prebuilt SageMaker framework containers, including PyTorch. This approach eliminates the need to redesign models or rewrite training logic while still benefiting from SageMaker's managed infrastructure, scalability, monitoring, and security.
Option A is incorrect because SageMaker built-in algorithms require adapting data formats and training logic to AWS-provided implementations, which would increase development effort and may not support proprietary domain logic.
Option C is also incorrect because building and maintaining a custom container requires additional effort for containerization, dependency management, security updates, and lifecycle maintenance-making it more complex than necessary.
Option D is not viable because purchasing models from AWS Marketplace would not support the company's proprietary datasets or unique domain knowledge embedded in existing models.
Therefore, using SageMaker AI script mode with prebuilt PyTorch containers is the fastest, most efficient, and AWS-recommended migration path that minimizes development effort while preserving existing workflows.
NEW QUESTION # 39
An ML engineer is using Amazon Quick Suite (previously known as Amazon QuickSight) anomaly detection to detect very high or very low machine operating temperatures compared to normal. The ML engineer sets the Severity parameter to Low and above. The ML engineer sets the Direction parameter to All.
What effect will the ML engineer observe in the anomaly detection results if the ML engineer changes the Direction parameter to Lower than expected?
Answer: C
Explanation:
In Amazon QuickSight anomaly detection, the Direction parameter controls which deviations from the expected baseline are flagged as anomalies. When Direction is set to All, QuickSight detects both higher-than- expected and lower-than-expected values. This results in a larger set of detected anomalies, increasing recall.
When the Direction parameter is changed to Lower than expected, the anomaly detection algorithm filters out all higher-than-expected anomalies and only reports unusually low values. Because the scope of detection is narrowed, the total number of detected anomalies decreases. As a result, fewer true anomalies are identified overall, which reduces recall.
Since fewer data points are evaluated as potential anomalies, the frequency of anomaly identification also decreases. This behavior is consistent with AWS documentation, which explains that directional filtering limits detection to a subset of deviations and therefore reduces overall anomaly counts.
Therefore, changing the Direction from All to Lower than expected leads to decreased anomaly identification frequency and decreased recall.
NEW QUESTION # 40
A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 41
An ML engineer needs to use an ML model to predict the price of apartments in a specific location.
Which metric should the ML engineer use to evaluate the model's performance?
Answer: B
Explanation:
When predicting continuous variables, such as apartment prices, it's essential to evaluate the model's performance using appropriate regression metrics. The Mean Absolute Error (MAE) is a widely used metric for this purpose.
Understanding Mean Absolute Error (MAE):
MAE measures the average magnitude of errors in a set of predictions, without considering their direction. It calculates the average absolute difference between predicted values and actual values, providing a straightforward interpretation of prediction accuracy.
Advantages of MAE:
* Interpretability: MAE is expressed in the same units as the target variable, making it easy to understand.
* Robustness to Outliers: Unlike metrics that square the errors (e.g., Mean Squared Error), MAE does not disproportionately penalize larger errors, making it more robust to outliers.
Comparison with Other Metrics:
* Accuracy, AUC, F1 Score: These metrics are designed for classification tasks, where the goal is to predict discrete labels. They are not suitable for regression problems involving continuous target variables.
* Mean Squared Error (MSE): While MSE also measures prediction errors, it squares the differences, giving more weight to larger errors. This can be useful in certain contexts but may be sensitive to outliers.
Conclusion:
For evaluating the performance of a model predicting apartment prices-a continuous variable-MAE is an appropriate and effective metric. It provides a clear indication of the average prediction error in the same units as the target variable, facilitating straightforward interpretation and comparison.
References:
Regression Metrics - GeeksforGeeks
Evaluation Metrics for Your Regression Model - Analytics Vidhya
Regression Metrics for Machine Learning - Machine Learning Mastery
NEW QUESTION # 42
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