An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day.
The ML engineer needs a scalable solution that minimizes costs when the model is not in use. The solution also must maintain the model's capacity to respond to requests during times of peak usage.
Which solution will meet these requirements?

1. A. Create AWS Lambda functions that have fixed concurrency to host the mode
2. B. Configure the Lambda functions to automatically scale based on the number of requests to the model.
3. C. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargat
4. D. Set a static number of tasks to handle requests during times of peak usage.
5. E. Deploy the model to an Amazon SageMaker endpoin
6. F. Deploy multiple copies of the model to the endpoin
7. G. Create an Application Load Balancer to route traffic between the different copies of the model at the endpoint.
8. H. Deploy the model to an Amazon SageMaker endpoin
9. I. Create SageMaker endpoint auto scaling policies that are based on Amazon CloudWatch metrics to adjust the number of instances dynamically.

Correct Answer: D

An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents.
Which solution will meet these requirements with the LEAST operational overhead?

1. A. Use the Natural Language Toolkit (NLTK) library on Amazon EC2 instances for text pre- processin
2. B. Use the Latent Dirichlet Allocation (LDA) algorithm to identify and extract relevant keywords.
3. C. Use Amazon SageMaker and the BlazingText algorith
4. D. Apply custom pre-processing steps for stemming and removal of stop word
5. E. Calculate term frequency-inverse document frequency (TF-IDF) scores to identify and extract relevant keywords.
6. F. Store the documents in an Amazon S3 bucke
7. G. Create AWS Lambda functions to process the documents and to run Python scripts for stemming and removal of stop word
8. H. Use bigram and trigram techniques to identify and extract relevant keywords.
9. I. Use Amazon Comprehend custom entity recognition and key phrase extraction to identify and extract relevant keywords.

Correct Answer: D

A company uses Amazon SageMaker for its ML workloads. The company's ML engineer receives a 50 MB Apache Parquet data file to build a fraud detection model. The file includes several correlated columns that are not required.
What should the ML engineer do to drop the unnecessary columns in the file with the LEAST effort?

1. A. Download the file to a local workstatio
2. B. Perform one-hot encoding by using a custom Python script.
3. C. Create an Apache Spark job that uses a custom processing script on Amazon EMR.
4. D. Create a SageMaker processing job by calling the SageMaker Python SDK.
5. E. Create a data flow in SageMaker Data Wrangle
6. F. Configure a transform step.

Correct Answer: D

An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account in the same Region.
Which solution will meet this requirement with the LEAST development effort?

1. A. Use Amazon S3 to make a copy of the mode
2. B. Transfer the copy to Account B.
3. C. Create a resource-based IAM polic
4. D. Use the Amazon Comprehend ImportModel API operation to copy the model to Account B.
5. E. Use AWS DataSync to replicate the model from Account A to Account B.
6. F. Create an AWS Site-to-Site VPN connection between Account A and Account to transfer the model.

Correct Answer: B

A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days.
The company has an Amazon SageMaker pipeline to retrain the model. An ML engineer needs to implement a solution to run the pipeline when new data is uploaded to the S3 bucket.
Which solution will meet these requirements with the LEAST operational effort?

1. A. Create an S3 Lifecycle rule to transfer the data to the SageMaker training instance and to initiate training.
2. B. Create an AWS Lambda function that scans the S3 bucke
3. C. Program the Lambda function to initiate the pipeline when new data is uploaded.
4. D. Create an Amazon EventBridge rule that has an event pattern that matches the S3 uploa
5. E. Configure the pipeline as the target of the rule.
6. F. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the pipeline when new data is uploaded.A company wants to develop an ML model by using tabular data from its customer
7. G. The data contains meaningful ordered features with sensitive information that should not be discarde
8. H. An ML engineer must ensure that the sensitive data is masked before another team starts to build the model.Which solution will meet these requirements?
9. I. Use Amazon Made to categorize the sensitive data.
10. J. Prepare the data by using AWS Glue DataBrew.
11. K. Run an AWS Batch job to change the sensitive data to random values.
12. L. Run an Amazon EMR job to change the sensitive data to random values.

Correct Answer: B