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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q22-Q27):
NEW QUESTION # 22
A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.
Select and order the pipeline ' s correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
* An S3 event notification invokes the pipeline when new data is uploaded.
* S3 Lifecycle rule invokes the pipeline when new data is uploaded.
* SageMaker retrains the model by using the data in the S3 bucket.
* The pipeline deploys the model to a SageMaker endpoint.
* The pipeline deploys the model to SageMaker Model Registry.
Answer:
Explanation:
Explanation:
Step 1: An S3 event notification invokes the pipeline when new data is uploaded.
Step 2: SageMaker retrains the model by using the data in the S3 bucket.
Step 3: The pipeline deploys the model to a SageMaker endpoint.
Step 1: An S3 Event Notification Invokes the Pipeline When New Data is Uploaded Why? The CI/CD pipeline should be triggered automatically whenever new training data is uploaded to Amazon S3. S3 event notifications can be configured to send events to AWS services like Lambda, which can then invoke AWS CodePipeline.
How? Configure the S3 bucket to send event notifications (e.g., s3:ObjectCreated:*) to AWS Lambda, which in turn triggers the CodePipeline.
Step 2: SageMaker Retrains the Model by Using the Data in the S3 Bucket Why? The uploaded data is used to retrain the ML model to incorporate new information and maintain performance. This step is critical to updating the model with fresh data.
How? Define a SageMaker training step in the CI/CD pipeline, which reads the training data from the S3 bucket and retrains the model.
Step 3: The Pipeline Deploys the Model to a SageMaker Endpoint
Why? Once retrained, the updated model must be deployed to a SageMaker endpoint to make it available for real-time inference.
How? Add a deployment step in the CI/CD pipeline, which automates the creation or update of the SageMaker endpoint with the retrained model.
Order Summary:
An S3 event notification invokes the pipeline when new data is uploaded.
SageMaker retrains the model by using the data in the S3 bucket.
The pipeline deploys the model to a SageMaker endpoint.
This configuration ensures an automated, efficient, and scalable CI/CD pipeline for continuous retraining and deployment of the ML model in Amazon SageMaker.
NEW QUESTION # 23
A company wants to use Amazon SageMaker to host an ML model that runs on CPU for real-time predictions. The model will have intermittent traffic during business hours and will have periods of no traffic after business hours. The company needs a solution that will serve inference requests in the most cost-effective manner. Which hosting option will meet these requirements?
- A. Deploy the model to a SageMaker Serverless Inference endpoint. Configure increased provisioned concurrency during business hours.
- B. Deploy the model to a SageMaker real-time endpoint. Add a schedule-based auto scaling policy to handle traffic surges during business hours.
- C. Deploy the model to a SageMaker Asynchronous Inference endpoint. Configure an auto scaling policy that scales in to zero outside business hours.
- D. Deploy the model to a SageMaker real-time endpoint. Create a scheduled AWS Lambda function that activates the endpoint during business hours only.
Answer: A
Explanation:
SageMaker Serverless Inference is the most cost-effective option for models with intermittent traffic. It automatically scales down to zero when idle, so no cost is incurred outside business hours. Configuring provisioned concurrency during business hours ensures low-latency responses when traffic is expected.
NEW QUESTION # 24
A company is using an AWS Lambda function to monitor the metrics from an ML model. An ML engineer needs to implement a solution to send an email message when the metrics breach a threshold.
Which solution will meet this requirement?
- A. Log the metrics from the Lambda function to Amazon CloudWatch. Configure a CloudWatch alarm to send the email message.
- B. Log the metrics from the Lambda function to AWS CloudTrail. Configure a CloudTrail trail to send the email message.
- C. Log the metrics from the Lambda function to Amazon CloudFront. Configure an Amazon CloudWatch alarm to send the email message.
- D. Log the metrics from the Lambda function to Amazon CloudWatch. Configure an Amazon CloudFront rule to send the email message.
Answer: D
Explanation:
Logging the metrics to Amazon CloudWatch allows the metrics to be tracked and monitored effectively.
CloudWatch Alarms can be configured to trigger when metrics breach a predefined threshold.
The alarm can be set to notify through Amazon Simple Notification Service (SNS), which can send email messages to the configured recipients.
This is the standard and most efficient way to achieve the desired functionality.
NEW QUESTION # 25
A company runs an ML model on Amazon SageMaker. The company uses an automatic process that makes API calls to create training jobs for the model. The company has new compliance rules that prohibit the collection of aggregated metadata from training jobs. Which solution will prevent SageMaker from collecting metadata from the training jobs?
- A. Ensure that training jobs are running in a private subnet in a custom VPC.
- B. Opt out of metadata tracking for any training job that is submitted.
- C. Encrypt the training data with an AWS Key Management Service (AWS KMS) customer managed key.
- D. Reconfigure the training jobs to use only AWS Nitro instances.
Answer: B
Explanation:
Amazon SageMaker automatically collects training job metadata, but you can opt out of metadata tracking when submitting a training job. This disables collection of aggregated metadata, ensuring compliance with rules that prohibit metadata collection.
NEW QUESTION # 26
An ML engineer is preparing a dataset that contains medical records to train an ML model to predict the likelihood of patients developing diseases.
The dataset contains columns for patient ID, age, medical conditions, test results, and a "Disease" target column.
How should the ML engineer configure the data to train the model?
- A. Remove the medical conditions and test results columns.
- B. Remove the patient ID column.
- C. Remove the age column.
- D. Remove the "Disease" target column.
Answer: B
Explanation:
Patient ID is a unique identifier and does not contain predictive information. Including it can cause the model to overfit by memorizing records rather than learning meaningful patterns.
AWS ML best practices recommend removing identifiers that are not causally related to the target variable.
Age, medical conditions, and test results are clinically relevant features and should be retained. The target column must remain for supervised learning.
Therefore, Option A is the correct and AWS-aligned choice.
NEW QUESTION # 27
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