Amazon MLA-C01 Online Practice Questions and Exam Preparation

Amazon MLA-C01 Online Questions & Answers

• Question 101:

  Which type of machine learning algorithm is best suited for detecting anomalies in network traffic?

  A. Supervised learning
  B. Unsupervised learning
  C. Reinforcement learning
  D. Semi-supervised learning
  B. Unsupervised learning

• Question 102:

  An ML engineer receives datasets that contain missing values, duplicates, and extreme outliers. The ML engineer must consolidate these datasets into a single data frame and must prepare the data for ML. Which solution will meet these requirements?

  A. Use Amazon SageMaker Data Wrangler to import the datasets and to consolidate them into a single data frame. Use the cleansing and enrichment functionalities to prepare the data.
  B. Use Amazon SageMaker Ground Truth to import the datasets and to consolidate them into a single data frame. Use the human-in-the- loop capability to prepare the data.
  C. Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon Q Developer to generate code snippets that will prepare the data.
  D. Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon SageMaker data labeling to prepare the data.
  A. Use Amazon SageMaker Data Wrangler to import the datasets and to consolidate them into a single data frame. Use the cleansing and enrichment functionalities to prepare the data.

• Question 103:

  A company needs to extract entities from a PDF document to build a classifier model, aiming to complete the process in the least amount of time. Which solution would most efficiently extract and store these entities?

  A. Use Amazon Comprehend to extract the entities. Store the output in Amazon S3.
  B. Use an open source AI optical character recognition (OCR) tool on Amazon SageMaker to extract the entities. Store the output in Amazon S3.
  C. Use Amazon Textract to extract the entities. Use Amazon Comprehend to convert the entities to text. Store the output in Amazon S3.
  D. Use Amazon Textract integrated with Amazon Augmented AI (Amazon A2I) to extract the entities. Store the output in Amazon S3.
  C. Use Amazon Textract to extract the entities. Use Amazon Comprehend to convert the entities to text. Store the output in Amazon S3.

• Question 104:

  An ML engineer has identified a class imbalance in an image classification training job. What steps should the ML engineer take to address this issue effectively?

  A. Reduce the size of the dataset.
  B. Transform some of the images in the dataset.
  C. Apply random oversampling on the dataset.
  D. Apply random data splitting on the dataset.
  C. Apply random oversampling on the dataset.

• Question 105:

  An ML engineer must ensure that a dataset complies with regulations regarding personally identifiable information (PII) before using it to train an ML model on Amazon SageMaker instances. Additionally, the solution must prevent SageMaker from accessing any PII. The approach should be as operationally efficient as possible.

  Which solution would best fulfill these requirements?

  A. Use the Amazon Comprehend DetectPiiEntities API call to redact the PII from the data. Store the data in an Amazon S3 bucket. Access the S3 bucket from the SageMaker instances for model training.
  B. Use the Amazon Comprehend DetectPiiEntities API call to redact the PII from the data. Store the data in an Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system to the SageMaker instances for model training.
  C. Use AWS Glue DataBrew to cleanse the dataset of PII. Store the data in an Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system to the SageMaker instances for model training.
  D. Use Amazon Macie for automatic discovery of PII in the data. Remove the PII. Store the data in an Amazon S3 bucket. Mount the S3 bucket to the SageMaker instances for model training.
  A. Use the Amazon Comprehend DetectPiiEntities API call to redact the PII from the data. Store the data in an Amazon S3 bucket. Access the S3 bucket from the SageMaker instances for model training.

• Question 106:

  A company wants to improve the sustainability of its ML operations.

  Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

  A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.
  B. Use Amazon SageMaker Ground Truth for data labeling.
  C. Deploy models by using AWS Lambda functions.
  D. Use AWS Trainium instances for training.
  E. Use PyTorch or TensorFlow with the distributed training option.
  A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.
  D. Use AWS Trainium instances for training.

• Question 107:

  An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions. Which metric nding should the ML engineer prioritize the MOST when choosing the model?

  A. Low precision
  B. High precision
  C. Low recall
  D. High recall
  D. High recall

• Question 108:

  A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker compute costs

  reach a specific threshold.

  Which solution will meet these requirements?

  A. Add resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Cost Explorer to send an alert when the threshold is reached.
  B. Add resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Budgets to send an alert when the threshold is reached.
  C. Add resource tagging by editing each user's IAM profile. Configure AWS Cost Explorer to send an alert when the threshold is reached.
  D. Add resource tagging by editing each user's IAM profile. Configure AWS Budgets to send an alert when the threshold is reached.
  B. Add resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Budgets to send an alert when the threshold is reached.

• Question 109:

  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

  notfication when the number of API call events breaches a threshold.

  Which solution will meet these requirements?

  A. Use SageMaker Debugger to track the inferences and to report metrics. Create a custom rule to provide a notfication when the threshold is breached.
  B. Use SageMaker Debugger to track the inferences and to report metrics. Use the tensor_variance built-in rule to provide a notfication when the threshold is breached.
  C. Log all the endpoint invocation API events by using AWS CloudTrail. Use an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notfication when the threshold is breached.
  D. Add the Invocations metric to an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notfication when the threshold is breached.
  D. Add the Invocations metric to an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notfication when the threshold is breached.

• Question 110:

  A company has an Amazon S3 bucket containing a large volume of files from various sources, all stored within the same S3 folder. The files include different formats such as CSV, JSON, XLSX, and Apache Parquet.

  An ML engineer is tasked with implementing a solution that leverages AWS Glue DataBrew to process this data. Additionally, the engineer must ensure that the final output is stored in Amazon S3, enabling AWS Glue to access and consume

  the data in the future.

  Which solution will effectively satisfy these requirements?

  A. Use DataBrew to process the existing S3 folder. Store the output in Apache Parquet format.
  B. Use DataBrew to process the existing S3 folder. Store the output in AWS Glue Parquet format.
  C. Separate the data into a different folder for each file type. Use DataBrew to process each folder individually. Store the output in Apache Parquet format.
  D. Separate the data into a different folder for each file type. Use DataBrew to process each folder individually. Store the output in AWS Glue Parquet format.
  C. Separate the data into a different folder for each file type. Use DataBrew to process each folder individually. Store the output in Apache Parquet format.