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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q23-Q28):

NEW QUESTION # 23
A company wants to reduce the cost of its containerized ML applications. The applications use ML models that run on Amazon EC2 instances, AWS Lambda functions, and an Amazon Elastic Container Service (Amazon ECS) cluster. The EC2 workloads and ECS workloads use Amazon Elastic Block Store (Amazon EBS) volumes to save predictions and artifacts.
An ML engineer must identify resources that are being used inefficiently. The ML engineer also must generate recommendations to reduce the cost of these resources.
Which solution will meet these requirements with the LEAST development effort?

A. Check AWS CloudTrail event history for the creation of the resources.
B. Add cost allocation tags to the resources. Activate the tags in AWS Billing and Cost Management.
C. Create code to evaluate each instance's memory and compute usage.
D. Run AWS Compute Optimizer.

Answer: D

Explanation:
AWS Compute Optimizer analyzes the resource usage of Amazon EC2 instances, ECS services, Lambda functions, and Amazon EBS volumes. It provides actionable recommendations to optimize resource utilization and reduce costs, such as resizing instances, moving workloads to Spot Instances, or changing volume types. This solution requires the least development effort because Compute Optimizer is a managed service that automatically generates insights and recommendations based on historical usage data.

NEW QUESTION # 24
An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training.
After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.
What should the ML engineer do to MINIMIZE the communication overhead between the instances?

A. Place the instances in the same VPC subnet. Store the data in a different AWS Region from where the instances are deployed.
B. Place the instances in the same VPC subnet but in different Availability Zones. Store the data in a different AWS Region from where the instances are deployed.
C. Place the instances in the same VPC subnet. Store the data in the same AWS Region and Availability Zone where the instances are deployed.
D. Place the instances in the same VPC subnet. Store the data in the same AWS Region but in a different Availability Zone from where the instances are deployed.

Answer: C

Explanation:
To minimize communication overhead during distributed training:
1. Same VPC Subnet: Ensures low-latency communication between training instances by keeping the network traffic within a single subnet.
2. Same AWS Region and Availability Zone: Reduces network latency further because cross-AZ communication incurs additional latency and costs.
3. Data in the Same Region and AZ: Ensures that the training data is accessed with minimal latency, improving performance during training.
This configuration optimizes communication efficiency and minimizes overhead.

NEW QUESTION # 25
A company has trained and deployed an ML model by using Amazon SageMaker. The company needs to implement a solution to record and monitor all the API call events for the SageMaker endpoint. The solution also must provide a notification when the number of API call events breaches a threshold.
Use SageMaker Debugger to track the inferences and to report metrics. Create a custom rule to provide a notification when the threshold is breached.
Which solution will meet these requirements?

A. Log all the endpoint invocation API events by using AWS CloudTrail. Use an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.
B. Use SageMaker Debugger to track the inferences and to report metrics. Create a custom rule to provide a notification when the threshold is breached.
C. Add the Invocations metric to an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.
D. Use SageMaker Debugger to track the inferences and to report metrics. Use the tensor_variance built-in rule to provide a notification when the threshold is breached.

Answer: C

Explanation:
Amazon SageMaker automatically tracks theInvocationsmetric, which represents the number of API calls made to the endpoint, inAmazon CloudWatch. By adding this metric to a CloudWatch dashboard, you can monitor the endpoint's activity in real-time. Setting up aCloudWatch alarmallows the system to send notifications whenever the API call events exceed the defined threshold, meeting both the monitoring and notification requirements efficiently.

NEW QUESTION # 26
A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data.
Which technique for feature engineering should the ML engineer use for the model?

A. Apply label encoding to the color categories. Automatically assign each color a unique integer.
B. One-hot encode the color categories to transform the color scheme feature into a binary matrix.
C. Implement padding to ensure that all color feature vectors have the same length.
D. Perform dimensionality reduction on the color categories.

Answer: B

Explanation:
One-hot encodingis the appropriate technique for transforming categorical data, such as color information, into a format suitable for input to a neural network. This technique creates a binary vector representation where each unique category (color) is represented as a separate binary column, ensuring that the model does not infer ordinal relationships between categories. This approach preserves the categorical nature of the data and avoids introducing unintended biases.

NEW QUESTION # 27
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?

A. Area Under the ROC Curve (AUC)
B. F1 score
C. Mean absolute error (MAE)
D. Accuracy

Answer: C

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.
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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 # 28
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Amazon MLA-C01 Reliable Braindumps Files - MLA-C01 Exam Test

Free PDF 2025 Updated Amazon MLA-C01 Reliable Braindumps Files

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q23-Q28):

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