MLA-C01 Free Practice Test

A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?

1. A. Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflow
2. B. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
3. C. Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflow
4. D. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
5. E. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflow
6. F. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
7. G. Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflow
8. H. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.

Correct Answer: C

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?

1. A. Use SageMaker Debugger to track the inferences and to report metric
2. B. Create a custom rule to provide a notification when the threshold is breached.
3. C. Use SageMaker Debugger to track the inferences and to report metric
4. D. Use the tensor_variance built-in rule to provide a notification when the threshold is breached.
5. E. Log all the endpoint invocation API events by using AWS CloudTrai
6. F. Use an Amazon CloudWatch dashboard for monitorin
7. G. Set up a CloudWatch alarm to provide notification when the threshold is breached.
8. H. Add the Invocations metric to an Amazon CloudWatch dashboard for monitorin
9. I. Set up a CloudWatch alarm to provide notification when the threshold is breached.

Correct Answer: D

A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.
Which solution will meet this requirement?

1. A. Configure the competitor's name as a blocked phrase in Amazon Q Business.
2. B. Configure an Amazon Q Business retriever to exclude the competitor??s name.
3. C. Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor's name.
4. D. Configure document attribute boosting in Amazon Q Business to deprioritize the competitor's name.

Correct Answer: A

A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine.
The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?

1. A. Deploy the model on Amazon SageMake
2. B. Create a set of AWS Lambda functions to identify and remove the sensitive data.
3. C. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargat
4. D. Create an AWS Batch job to identify and remove the sensitive data.
5. E. Use Amazon Macie to identify the sensitive dat
6. F. Create a set of AWS Lambda functions to remove the sensitive data.
7. G. Use Amazon Comprehend to identify the sensitive dat
8. H. Launch Amazon EC2 instances to remove the sensitive data.

Correct Answer: C

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