Amazon MLA-C01 Online Practice Questions and Exam Preparation

Amazon MLA-C01 Online Questions & Answers

• Question 41:

  An ML engineer needs to use Amazon SageMaker to ne-tune a large language model (LLM) for text summarization. The ML engineer must follow a low-code no-code (LCNC) approach. Which solution will meet these requirements?

  A. Use SageMaker Studio to ne-tune an LLM that is deployed on Amazon EC2 instances.
  B. Use SageMaker Autopilot to ne-tune an LLM that is deployed by a custom API endpoint.
  C. Use SageMaker Autopilot to ne-tune an LLM that is deployed on Amazon EC2 instances.
  D. Use SageMaker Autopilot to ne-tune an LLM that is deployed by SageMaker JumpStart.
  D. Use SageMaker Autopilot to ne-tune an LLM that is deployed by SageMaker JumpStart.

• Question 42:

  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?

  A. Use the Natural Language Toolkit (NLTK) library on Amazon EC2 instances for text pre-processing. Use the Latent Dirichlet Allocation (LDA) algorithm to identify and extract relevant keywords.
  B. Use Amazon SageMaker and the BlazingText algorithm. Apply custom pre-processing steps for stemming and removal of stop words. Calculate term frequency-inverse document frequency (TF-IDF) scores to identify and extract relevant keywords.
  C. Store the documents in an Amazon S3 bucket. Create AWS Lambda functions to process the documents and to run Python scripts for stemming and removal of stop words. Use bigram and trigram techniques to identify and extract relevant keywords.
  D. Use Amazon Comprehend custom entity recognition and key phrase extraction to identify and extract relevant keywords.
  D. Use Amazon Comprehend custom entity recognition and key phrase extraction to identify and extract relevant keywords.

• Question 43:

  An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of the data quality of the models. The ML engineer must receive

  alerts when changes in data quality occur.

  Which solution will meet these requirements?

  A. Deploy the models by using scheduled AWS Glue jobs. Use Amazon CloudWatch alarms to monitor the data quality and to send alerts.
  B. Deploy the models by using scheduled AWS Batch jobs. Use AWS CloudTrail to monitor the data quality and to send alerts.
  C. Deploy the models by using Amazon Elastic Container Service (Amazon ECS) on AWS Fargate. Use Amazon EventBridge to monitor the data quality and to send alerts.
  D. Deploy the models by using Amazon SageMaker batch transform. Use SageMaker Model Monitor to monitor the data quality and to send alerts.
  D. Deploy the models by using Amazon SageMaker batch transform. Use SageMaker Model Monitor to monitor the data quality and to send alerts.

• Question 44:

  A company's ML engineer has deployed an ML model for sentiment analysis to an Amazon SageMaker endpoint. The ML engineer needs to explain to company stakeholders how the model makes predictions. Which solution will provide an explanation for the model's predictions?

  A. Use SageMaker Model Monitor on the deployed model.
  B. Use SageMaker Clarify on the deployed model.
  C. Show the distribution of inferences from A/B testing in Amazon CloudWatch.
  D. Add a shadow endpoint. Analyze prediction differences on samples.
  B. Use SageMaker Clarify on the deployed model.

• Question 45:

  A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes. Which solution on AWS will detect duplicates in the dataset with the LEAST code development?

  A. Use Amazon Mechanical Turk jobs to detect duplicates.
  B. Use Amazon QuickSight ML Insights to build a custom deduplication model.
  C. Use Amazon SageMaker Data Wrangler to pre-process and detect duplicates.
  D. Use the AWS Glue FindMatches transform to detect duplicates.
  D. Use the AWS Glue FindMatches transform to detect duplicates.

• Question 46:

  A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to predict whether new customers will require long-term support. Which modeling approach should the company use to meet this requirement?

  A. Anomaly detection
  B. Linear regression
  C. Logistic regression
  D. Semantic segmentation
  C. Logistic regression

• Question 47:

  A company is planning to use Amazon SageMaker to make classication ratings that are based on images. The company has 6 of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM). The SVM is in

  the same VPC as SageMaker.

  An ML engineer must make the training data accessible for ML models that are in the SageMaker environment.

  Which solution will meet these requirements?

  A. Mount the FSx for ONTAP file system as a volume to the SageMaker Instance.
  B. Create an Amazon S3 bucket. Use Mountpoint for Amazon S3 to link the S3 bucket to the FSx for ONTAP file system.
  C. Create a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
  D. Create a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
  A. Mount the FSx for ONTAP file system as a volume to the SageMaker Instance.

• Question 48:

  An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning. The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker domain.

  Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Choose two.)

  A. The ML engineer and the Canvas user must be in separate SageMaker domains.
  B. The Canvas user must have permissions to access the S3 bucket where the model artifacts are stored.
  C. The model must be registered in the SageMaker Model Registry.
  D. The ML engineer must host the model on AWS Marketplace.
  E. The ML engineer must deploy the model to a SageMaker endpoint.
  B. The Canvas user must have permissions to access the S3 bucket where the model artifacts are stored.
  C. The model must be registered in the SageMaker Model Registry.

• Question 49:

  A company has a binary classification model in production. An ML engineer needs to develop a new version of the model. The new model version must maximize correct predictions of positive labels and negative labels. The ML engineer

  must use a metric to recalibrate the model to meet these requirements.

  Which metric should the ML engineer use for the model recalibration?

  A. Accuracy
  B. Precision
  C. Recall
  D. specificity
  A. Accuracy

• Question 50:

  A company has an ML model that needs to run one time each night to predict stock values. The model input is 3 MB of data that is collected during the current day. The model produces the predictions for the next day. The prediction process takes less than 1 minute to nish running. How should the company deploy the model on Amazon SageMaker to meet these requirements?

  A. Use a multi-model serverless endpoint. Enable caching.
  B. Use an asynchronous inference endpoint. Set the InitialInstanceCount parameter to 0.
  C. Use a real-time endpoint. Configure an auto scaling policy to scale the model to 0 when the model is not in use.
  D. Use a serverless inference endpoint. Set the MaxConcurrency parameter to 1.
  D. Use a serverless inference endpoint. Set the MaxConcurrency parameter to 1.