Amazon MLA-C01 Exam Questions Answers

Amazon MLA-C01 Study Questions for Exam 2026

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AWS Certified Machine Learning Engineer - Associate Exam MLA-C01 Questions and Answers

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Page: 1 / 17
Total Questions: 85

Questions & Answers PDF

• Question 1

  

  A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter.What should the company do to MINIMIZE false positives?

  • Set the value of the weight decay hyperparameter to zero.
  • Increase the number of training epochs.
  • Increase the value of the target_precision hyperparameter.
  • Change the value of the predictorjype hyperparameter to regressor.

  Reveal Answer Answer: C Next Question

• Question 2

  

  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 finish running. How should the company deploy the model on Amazon SageMaker to meet these requirements?

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

  Reveal Answer Answer: D Next Question

• Question 3

  

  A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize theexisting models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.Which solution will meet these requirements?

  • Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.
  • Create a model group for each category. Move the existing models into these category model groups.
  • Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
  • Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.

  Reveal Answer Answer: A Next Question

• Question 4

  

  An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed-circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.Which solution will improve the model's accuracy in the LEAST amount of time?

  • Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.
  • Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.
  • Recreate the training dataset by using the Data Wrangler enhance image contrast transform. Specify the Gamma contrast option.
  • Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.

  Reveal Answer Answer: B Next Question

• Question 5

  

  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?

  • 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.
  • 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.
  • 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.
  • Use Amazon Comprehend custom entity recognition and key phrase extraction to identify and extract relevant keywords.

  Reveal Answer Answer: D Next Question

• Question 6

  

  A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second.The company needs to implement a scalable solution on AWS to identify anomalous data points. Which solution will meet these requirements with the LEAST operational overhead?

  • Ingest real-time data into Amazon Kinesis data streams. Use the built-in RANDOM_CUT_FOREST function in Amazon Managed Service for Apache Flink to process the data streams and to detect data anomalies.
  • Ingest real-time data into Amazon Kinesis data streams. Deploy an Amazon SageMaker endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
  • Ingest real-time data into Apache Kafka on Amazon EC2 instances. Deploy an Amazon SageMaker endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
  • Send real-time data to an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Create an AWS Lambda function to consume the queue messages. Program the Lambda function to start an AWS Glue extract, transform, and load (ETL) job for batch processing and anomaly detection.

  Reveal Answer Answer: A Next Question

• Question 7

  

  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?

  • Anomaly detection
  • Linear regression
  • Logistic regression
  • Semantic segmentation

  Reveal Answer Answer: C Next Question

• Question 8

  

  An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm. The model classifies transactions as either fraudulent or legitimate.During testing, the model excels at identifying fraud in the training dataset. However, the model is inefficient at identifying fraud in new and unseen transactions.What should the ML engineer do to improve the fraud detection for new transactions?

  • Increase the learning rate.
  • Remove some irrelevant features from the training dataset.
  • Increase the value of the max_depth hyperparameter.
  • Decrease the value of the max_depth hyperparameter.

  Reveal Answer Answer: D Next Question

• Question 9

  

  A company has an application that uses different APIs to generate embeddings for input text. The company needs to implement a solution to automatically rotate the API tokens every 3 months. Which solution will meet this requirement?

  • Store the tokens in AWS Secrets Manager. Create an AWS Lambda function to perform the rotation.
  • Store the tokens in AWS Systems Manager Parameter Store. Create an AWS Lambda function to perform the rotation.
  • Store the tokens in AWS Key Management Service (AWS KMS). Use an AWS managed key to perform the rotation.
  • Store the tokens in AWS Key Management Service (AWS KMS). Use an AWS owned key to perform the rotation.

  Reveal Answer Answer: A Next Question

• Question 10

  

  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?

  • Create a SageMaker real-time inference endpoint. Configure auto scaling. Configure the endpoint to present the existing model.
  • 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.
  • 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.
  • 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.

  Reveal Answer Answer: A Next Question

Page: 1 / 17
Total Questions: 85

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