Amazon MLA-C01 Online Practice Questions and Exam Preparation

Amazon MLA-C01 Online Questions & Answers

• Question 71:

  A company has an ML model that generates text descriptions based on images that customers upload to the company's website. The images can be up to 50 MB in total size. An ML engineer decides to store the images in an Amazon S3

  bucket. The ML engineer must implement a processing solution that can scale to accommodate changes in demand.

  Which solution will meet these requirements with the LEAST operational overhead?

  A. Create an Amazon SageMaker batch transform job to process all the images in the S3 bucket.
  B. Create an Amazon SageMaker Asynchronous Inference endpoint and a scaling policy. Run a script to make an inference request for each image.
  C. Create an Amazon Elastic Kubernetes Service (Amazon EKS) cluster that uses Karpenter for auto scaling. Host the model on the EKS cluster. Run a script to make an inference request for each image.
  D. Create an AWS Batch job that uses an Amazon Elastic Container Service (Amazon ECS) cluster. Specify a list of images to process for each AWS Batch job.
  B. Create an Amazon SageMaker Asynchronous Inference endpoint and a scaling policy. Run a script to make an inference request for each image.

• Question 72:

  An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3. The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data. Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data. Which solution will meet this requirement with the LEAST operational effort?

  A. Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.
  B. Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.
  C. Use AWS Glue DataBrew built-in features to oversample the minority class.
  D. Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.
  D. Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.

• Question 73:

  A company needs to host a custom ML model to perform forecast analysis. The forecast analysis will occur with predictable and sustained load during the same 2-hour period every day. Multiple invocations during the analysis period will

  require quick responses. The company needs AWS to manage the underlying infrastructure and any auto scaling activities.

  Which solution will meet these requirements?

  A. Schedule an Amazon SageMaker batch transform job by using AWS Lambda.
  B. Configure an Auto Scaling group of Amazon EC2 instances to use scheduled scaling.
  C. Use Amazon SageMaker Serverless Inference with provisioned concurrency.
  D. Run the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster on Amazon EC2 with pod auto scaling.
  C. Use Amazon SageMaker Serverless Inference with provisioned concurrency.

• Question 74:

  A company wants to develop an ML model by using tabular data from its customers. The data contains meaningful ordered features with sensitive information that should not be discarded. 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?

  A. Use Amazon Made to categorize the sensitive data.
  B. Prepare the data by using AWS Glue DataBrew.
  C. Run an AWS Batch job to change the sensitive data to random values.
  D. Run an Amazon EMR job to change the sensitive data to random values.
  B. Prepare the data by using AWS Glue DataBrew.

• Question 75:

  A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product

  by reviewing customer sentiments about the product.

  Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

  A. Use Amazon Rekognition to analyze sentiments of the chat conversations.
  B. Train a Naive Bayes classi er to analyze sentiments of the chat conversations.
  C. Use Amazon Comprehend to analyze sentiments of the chat conversations.
  D. Use random forests to classify sentiments of the chat conversations.
  C. Use Amazon Comprehend to analyze sentiments of the chat conversations.

• Question 76:

  A company is seeking to develop a machine learning model capable of identifying items within images and determining their locations. Which Amazon SageMaker algorithm is best suited to fulfill these requirements?

  A. Image classification
  B. XGBoost
  C. Object detection
  D. K-nearest neighbors (k-NN)
  C. Object detection

• Question 77:

  A company is running ML models on premises by using custom Python scripts and proprietary datasets. The company is using PyTorch. The model building requires unique domain knowledge. The company needs to move the models to

  AWS.

  Which solution will meet these requirements with the LEAST effort?

  A. Use SageMaker built-in algorithms to train the proprietary datasets.
  B. Use SageMaker script mode and premade images for ML frameworks.
  C. Build a container on AWS that includes custom packages and a choice of ML frameworks.
  D. Purchase similar production models through AWS Marketplace.
  B. Use SageMaker script mode and premade images for ML frameworks.

• Question 78:

  A manufacturing company employs a machine learning model to assess product quality, generating an output of either "Passed" or "Failed." Robots utilize this model to analyze photos on the assembly line and sort products into these two categories.

  Which two metrics should the company use to effectively evaluate the model's performance?

  A. Precision and recall
  B. Root mean square error (RMSE) and mean absolute percentage error (MAPE)
  C. Accuracy and F1 score
  D. Bilingual Evaluation Understudy (BLEU) score
  E. Perplexity
  A. Precision and recall
  C. Accuracy and F1 score

• Question 79:

  A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment. The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand. How should the company deploy the model into production to meet these requirements?

  A. Create a SageMaker real-time inference endpoint. Configure auto scaling. Configure the endpoint to present the existing model.
  B. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster. Use ECS scheduled scaling that is based on the CPU of the ECS cluster.
  C. Install SageMaker Operator on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. Deploy the model in Amazon EKS. Set horizontal pod auto scaling to scale replicas based on the memory metric.
  D. Use Spot Instances with a Spot Fleet behind an Application Load Balancer (ALB) for inferences. Use the ALBRequestCountPerTarget metric as the metric for auto scaling.
  A. Create a SageMaker real-time inference endpoint. Configure auto scaling. Configure the endpoint to present the existing model.

• Question 80:

  HOTSPOT

  An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:

  1. Feature splitting

  2. Logarithmic transformation

  3. One-hot encoding

  4. Standardized distribution

  Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)