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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q299-Q304):

NEW QUESTION # 299
A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.
Which architecture changes would ensure that provisioned resources are being utilized effectively?

A. Redeploy the model on a P3dn instance.
B. Redeploy the model as a batch transform job on an M5 instance.
C. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.
D. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.

Answer: C

NEW QUESTION # 300
A company deployed a machine learning (ML) model on the company website to predict real estate prices.
Several months after deployment, an ML engineer notices that the accuracy of the model has gradually decreased.
The ML engineer needs to improve the accuracy of the model. The engineer also needs to receive notifications for any future performance issues.
Which solution will meet these requirements?

A. Use only data from the previous several months to perform incremental training to update the model.Use Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.
B. Use Amazon SageMaker Model Governance. Configure Model Governance to automatically adjust model hyper para meters. Create a performance threshold alarm in Amazon CloudWatch to send notifications.
C. Perform incremental training to update the model. Activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.
D. Use Amazon SageMaker Debugger with appropriate thresholds. Configure Debugger to send Amazon CloudWatch alarms to alert the team Retrain the model by using only data from the previous several months.

Answer: C

Explanation:
The best solution to improve the accuracy of the model and receive notifications for any future performance issues is to perform incremental training to update the model and activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications. Incremental training is a technique that allows you to update an existing model with new data without retraining the entire model from scratch. This can save time and resources, and help the model adapt to changing data patterns. Amazon SageMaker Model Monitor is a feature that continuously monitors the quality of machine learning models in production and notifies you when there are deviations in the model quality, such as data drift and anomalies. You can set up alerts that trigger actions, such as sending notifications to Amazon Simple Notification Service (Amazon SNS) topics, when certain conditions are met.
Option B is incorrect because Amazon SageMaker Model Governance is a set of tools that help you implement ML responsibly by simplifying access control and enhancing transparency. It does not provide a mechanism to automatically adjust model hyperparameters or improve model accuracy.
Option C is incorrect because Amazon SageMaker Debugger is a feature that helps you debug and optimize your model training process by capturing relevant data and providing real-time analysis. However, using Debugger alone does not update the model or monitor its performance in production. Also, retraining the model by using only data from the previous several months may not capture the full range of data variability and may introduce bias or overfitting.
Option D is incorrect because using only data from the previous several months to perform incremental training may not be sufficient to improve the model accuracy, as explained above. Moreover, this option does not specify how to activate Amazon SageMaker Model Monitor or configure the alerts and notifications.
Incremental training
Amazon SageMaker Model Monitor
Amazon SageMaker Model Governance
Amazon SageMaker Debugger

NEW QUESTION # 301
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

A. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
B. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.
C. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
D. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.

Answer: C

Explanation:
To deploy a model that was trained locally to Amazon SageMaker, the steps are:
Build the Docker image with the inference code. The inference code should include the model loading, data preprocessing, prediction, and postprocessing logic. The Docker image should also include the dependencies and libraries required by the inference code and the model.
Tag the Docker image with the registry hostname and upload it to Amazon ECR. Amazon ECR is a fully managed container registry that makes it easy to store, manage, and deploy container images. The registry hostname is the Amazon ECR registry URI for your account and Region. You can use the AWS CLI or the Amazon ECR console to tag and push the Docker image to Amazon ECR.
Create a SageMaker model entity that points to the Docker image in Amazon ECR and the model artifacts in Amazon S3. The model entity is a logical representation of the model that contains the information needed to deploy the model for inference. The model artifacts are the files generated by the model training process, such as the model parameters and weights. You can use the AWS CLI, the SageMaker Python SDK, or the SageMaker console to create the model entity.
Create an endpoint configuration that specifies the instance type and number of instances to use for hosting the model. The endpoint configuration also defines the production variants, which are the different versions of the model that you want to deploy. You can use the AWS CLI, the SageMaker Python SDK, or the SageMaker console to create the endpoint configuration.
Create an endpoint that uses the endpoint configuration to deploy the model. The endpoint is a web service that exposes an HTTP API for inference requests. You can use the AWS CLI, the SageMaker Python SDK, or the SageMaker console to create the endpoint.
References:
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Deploy a Model on Amazon SageMaker
AWS Machine Learning Training - Use Your Own Inference Code with Amazon SageMaker Hosting Services

NEW QUESTION # 302
A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the team requires better performance How should the records be stored in Amazon S3 to improve query performance?

A. Compressed JSON
B. Parquet files
C. CSV files
D. RecordIO

Answer: B

Explanation:
Parquet is a columnar storage format that can store data in a compressed and efficient way. Parquet files can improve query performance by reducing the amount of data that needs to be scanned, as only the relevant columns are read from the files. Parquet files can also support predicate pushdown, which means that the filtering conditions are applied at the storage level, further reducing the data that needs to be processed.
Parquet files are compatible with Amazon Athena, which can leverage the benefits of the columnar format and provide faster and cheaper queries. Therefore, the records should be stored in Parquet files in Amazon S3 to improve query performance.
Columnar Storage Formats - Amazon Athena
Parquet SerDe - Amazon Athena
Optimizing Amazon Athena Queries - Amazon Athena
Parquet - Apache Software Foundation

NEW QUESTION # 303
A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords.
Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?

A. Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.
B. Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.
C. Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.
D. Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.

Answer: D

Explanation:
Amazon SageMaker script mode is a feature that allows users to use training scripts similar to those they would use outside SageMaker with SageMaker's prebuilt containers for various frameworks such as TensorFlow. Script mode supports reading data from Amazon S3 buckets without requiring any changes to the training script. Therefore, option B is the best method of providing training data to Amazon SageMaker that would meet the business requirements with the least development overhead.
Option A is incorrect because using a local path of the data would not be scalable or reliable, as it would depend on the availability and capacity of the local storage. Moreover, using a local path of the data would not leverage the benefits of Amazon S3, such as durability, security, and performance. Option C is incorrect because rewriting the train.py script to convert TFRecords to protobuf would require additional development effort and complexity, as well as introduce potential errors and inconsistencies in the data format. Option D is incorrect because preparing the data in the format accepted by Amazon SageMaker would also require additional development effort and complexity, as well as involve using additional services such as AWS Glue or AWS Lambda, which would increase the cost and maintenance of the solution.
References:
Bring your own model with Amazon SageMaker script mode
GitHub - aws-samples/amazon-sagemaker-script-mode
Deep Dive on TensorFlow training with Amazon SageMaker and Amazon S3
amazon-sagemaker-script-mode/generate_cifar10_tfrecords.py at master

NEW QUESTION # 304
......

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