What is the Passing Score for Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Exam?

The passing score for the Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam is set by Amazon AWS and published in the official exam guide for the AWS Certified Machine Learning Engineer - Associate MLA-C01 certification.

What is the Competency Level required for Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Exam?

The Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam is an intermediate level exam. It is designed for candidates who have the knowledge and practical understanding expected at the associate level of the Amazon AWS certification program.

What is the Question Format of Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Exam?

The Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam typically includes multiple choice and multiple response questions based on the topics in the official syllabus.

What is the Recommended Experience for Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Exam?

For the Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam, around 1 year of hands-on experience in the field is recommended. Candidates should also review the official syllabus and practice with sample questions before scheduling the exam.

What is the Expected Retirement Date of Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Exam?

The Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam retired on 2026-09-28, and its successor is MLA-C02.

What is the Difficulty Level of Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Exam?

The difficulty level of the Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam is considered to be moderate. It is recommended to have a good understanding of the topics covered in the official syllabus.

What are the Sample Questions of Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Exam?

You can try sample questions of the Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam for free on Study4Pass. The free practice test on this page includes sample questions with answers and explanations from the full set of 271 practice questions.

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Free AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 – Amazon AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Practice Test Question

Q: What is the Duration of Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Exam?

The duration of the Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam is defined by Amazon AWS in the official exam guide for the AWS Certified Machine Learning Engineer - Associate MLA-C01 certification. Candidates should confirm the current duration on the official exam page before scheduling.

Q: What is the Number of Questions Asked in Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Exam?

The number of questions on the Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam is defined by Amazon AWS in the official exam guide. The Study4Pass practice test for this exam contains 271 practice questions with answers and explanations.

Q: How Can You Take Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Exam?

You can register for the Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam through the Amazon AWS certification website and take it at an authorized testing center or, where Amazon AWS offers it, as an online proctored exam.

Q: What is the Cost of Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Exam?

The cost of the Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam is set by Amazon AWS and can vary by region. The current exam fee is published on the official Amazon AWS certification page for the AWS Certified Machine Learning Engineer - Associate MLA-C01 certification.

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Showing 1–24 of 24 questions

Question 1 (Topic 1)

HOTSPOT - An ML engineer needs to use Amazon SageMaker Feature Store to create and manage features to train a model. Select and order the steps from the following list to create and use the features in Feature Store. Each step should be selected one time. (Select and order three.) • Access the store to build datasets for training. • Create a feature group. • Ingest the records.

Answer is in the explanation below.

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Question 2 (Topic 1)

HOTSPOT - An ML engineer is building a generative AI application on Amazon Bedrock by using large language models (LLMs). Select the correct generative AI term from the following list for each description. Each term should be selected one time or not at all. (Select three.) • Embedding • Retrieval Augmented Generation (RAG) • Temperature • Token

Answer is in the explanation below.

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Question 3 (Topic 1)

A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict. The company needs to use the dataset in a solution to determine if a model can predict the target variable. Which solution will provide this information with the LEAST development effort?

Select an option, then click Submit answer.

○ Create a new model by using Amazon SageMaker Autopilot. Report the model's achieved performance.
○ Implement custom scripts to perform data pre-processing, multiple linear regression, and performance evaluation. Run the scripts on Amazon EC2 instances.
○ Configure Amazon Macie to analyze the dataset and to create a model. Report the model's achieved performance.
○ Select a model from Amazon Bedrock. Tune the model with the data. Report the model's achieved performance.

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Question 4 (Topic 1)

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?

Select an option, then click Submit answer.

○ 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.

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Question 5 (Topic 1)

An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host. Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?

Select an option, then click Submit answer.

○ AWS::SageMaker::Model
○ AWS::SageMaker::Endpoint
○ AWS::SageMaker::NotebookInstance
○ AWS::SageMaker::Pipeline

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Question 6 (Topic 1)

An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured data. The company's ML engineers are assigned to specific advertisement campaigns. The ML engineers must interact with the data through Amazon Athena and by browsing the data directly in an Amazon S3 bucket. The ML engineers must have access to only the resources that are specific to their assigned advertisement campaigns. Which solution will meet these requirements in the MOST operationally efficient way?

Select an option, then click Submit answer.

○ Configure IAM policies on an AWS Glue Data Catalog to restrict access to Athena based on the ML engineers' campaigns.
○ Store users and campaign information in an Amazon DynamoDB table. Configure DynamoDB Streams to invoke an AWS Lambda function to update S3 bucket policies.
○ Use Lake Formation to authorize AWS Glue to access the S3 bucket. Configure Lake Formation tags to map ML engineers to their campaigns.
○ Configure S3 bucket policies to restrict access to the S3 bucket based on the ML engineers' campaigns.

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Question 7 (Topic 1)

A company is using an AWS Lambda function to monitor the metrics from an ML model. An ML engineer needs to implement a solution to send an email message when the metrics breach a threshold. Which solution will meet this requirement?

Select an option, then click Submit answer.

○ Log the metrics from the Lambda function to AWS CloudTrail. Configure a CloudTrail trail to send the email message.
○ Log the metrics from the Lambda function to Amazon CloudFront. Configure an Amazon CloudWatch alarm to send the email message.
○ Log the metrics from the Lambda function to Amazon CloudWatch. Configure a CloudWatch alarm to send the email message.
○ Log the metrics from the Lambda function to Amazon CloudWatch. Configure an Amazon CloudFront rule to send the email message.

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Question 8 (Topic 1)

A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks. What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?

Select an option, then click Submit answer.

○ Adjust the model's parameters and hyperparameters.
○ Initiate a manual Model Monitor job that uses the most recent production data.
○ Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.
○ Include additional data in the existing training set for the model. Retrain and redeploy the model.

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Reference / correct answer:

Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.

Most accepted answer: C. Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.

Community votes: C=5, D=2

Selected Answer: D This question lacks sufficient context. If Model Monitor is getting errors after the update, do you want to C) establish that as the new baseline or D) retrain the model to adhere to the existing baseline? I'm going to choose D as there's no indicating we should "change the way we do business" by establishing a new model monitor baseline to conform with the new model's behavior. My $.02 upvoted 1 times

Selected Answer: C Agree with C. upvoted 1 times

Selected Answer: C Amazon SageMaker Model Monitor: Continuously monitors model endpoints to detect issues like: Data quality drift Model quality drift Bias drift Feature attribution drift Baseline: Model Monitor compares incoming data against a baseline dataset that represents "normal" or expected data distributions. If the data distribution changes after a model update, the old baseline may no longer be valid—leading to false positives in data quality issues. upvoted 2 times

Selected Answer: C agree with GiorgioGss - If the problems start appearing "After a model update" then C is the only valid option. upvoted 2 times

Selected Answer: D the model needs to be retrained upvoted 2 times

Question 9 (Topic 1)

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?

Select an option, then click Submit answer.

○ 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.

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Question 10 (Topic 1)

A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other business groups. The company uses a single AWS account and stores all the training data in Amazon S3 buckets. All ML model training occurs in Amazon SageMaker. Which solution will provide the ML engineers with the appropriate access?

Select an option, then click Submit answer.

○ Enable S3 bucket versioning.
○ Configure S3 Object Lock settings for each user.
○ Add cross-origin resource sharing (CORS) policies to the S3 buckets.
○ Create IAM policies. Attach the policies to IAM users or IAM roles.

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Question 11 (Topic 1)

An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training. After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances. What should the ML engineer do to MINIMIZE the communication overhead between the instances?

Select an option, then click Submit answer.

○ Place the instances in the same VPC subnet. Store the data in a different AWS Region from where the instances are deployed.
○ Place the instances in the same VPC subnet but in different Availability Zones. Store the data in a different AWS Region from where the instances are deployed.
○ Place the instances in the same VPC subnet. Store the data in the same AWS Region and Availability Zone where the instances are deployed.
○ Place the instances in the same VPC subnet. Store the data in the same AWS Region but in a different Availability Zone from where the instances are deployed.

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Question 12 (Topic 1)

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?

Select an option, then click Submit answer.

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

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Question 13 (Topic 1)

A company has an Amazon S3 bucket that contains 1 ТВ of files from different sources. The S3 bucket contains the following file types in the same S3 folder: CSV, JSON, XLSX, and Apache Parquet. An ML engineer must implement a solution that uses AWS Glue DataBrew to process the data. The ML engineer also must store the final output in Amazon S3 so that AWS Glue can consume the output in the future. Which solution will meet these requirements?

Select an option, then click Submit answer.

○ Use DataBrew to process the existing S3 folder. Store the output in Apache Parquet format.
○ Use DataBrew to process the existing S3 folder. Store the output in AWS Glue Parquet format.
○ 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.
○ 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.

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Reference / correct answer:

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.

Most accepted answer: 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.

Community votes: A=3, C=5

Selected Answer: C ✅ Explanation: Problem Summary: The data in S3 is mixed file formats: CSV, JSON, XLSX, and Parquet — all in one folder. You need to use AWS Glue DataBrew to process the data. The processed data must be stored in S3 for AWS Glue to consume later. Key Considerations: DataBrew Input Requirements: DataBrew datasets must be in a consistent format (CSV, JSON, XLSX, or Parquet). DataBrew cannot process mixed formats in a single dataset. You must split the data by format. DataBrew Output Format: Apache Parquet is preferred for: Efficient storage Better performance with AWS Glue and other analytics tools Columnar storage benefits in querying and transformations "AWS Glue Parquet format" does not exist — this is a distractor in the answer options. upvoted 6 times

Selected Answer: C DataBrew requires s3 buckets contains same format of data so it is c upvoted 1 times

Selected Answer: C Confusing, but I'd go with C. upvoted 1 times

Selected Answer: C AWS Glue DataBrew works best when processing files of the same type together. It's designed to apply consistent transformations to datasets with similar structures. When working with multiple file formats (CSV, JSON, XLSX, and Parquet), separating them by type allows for format-specific preprocessing before merging the data. upvoted 3 times

Selected Answer: A 🔑 Keyword: Process mixed file types with AWS Glue DataBrew & store for AWS Glue ✅ Correct Answer: A. Use DataBrew to process the existing S3 folder. Store the output in Apache Parquet format. Why? AWS Glue performs best with Parquet because it is optimized for analytical queries. No need to split data into separate folders—DataBrew can handle mixed file types. Why Others Are Wrong? ❌ B. "AWS Glue Parquet format" is not a valid term. Apache Parquet is the correct format. ❌ C & D. Separating files into different folders is unnecessary—DataBrew can process multiple formats in a single folder. upvoted 2 times

Question 14 (Topic 1)

An ML engineer needs to encrypt all data in transit when an ML training job runs. The ML engineer must ensure that encryption in transit is applied to processes that Amazon SageMaker uses during the training job. Which solution will meet these requirements?

Select an option, then click Submit answer.

○ Encrypt communication between nodes for batch processing.
○ Encrypt communication between nodes in a training cluster.
○ Specify an AWS Key Management Service (AWS KMS) key during creation of the training job request.
○ Specify an AWS Key Management Service (AWS KMS) key during creation of the SageMaker domain.

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Question 15 (Topic 1)

A company needs to extract entities from a PDF document to build a classifier model. Which solution will extract and store the entities in the LEAST amount of time?

Select an option, then click Submit answer.

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

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Reference / correct answer:

Use Amazon Comprehend to extract the entities. Store the output in Amazon S3.

Most accepted answer: A. Use Amazon Comprehend to extract the entities. Store the output in Amazon S3.

Community votes: A=6, C=3

Selected Answer: A https://aws.amazon.com/blogs/aws/now-process-pdfs-word-documents-and-images-with-amazon-comprehend-for-idp/ Amazon Comprehend feature for intelligent document processing (IDP). This feature allows you to classify and extract entities from PDF documents, Microsoft Word files, and images directly from Amazon Comprehend without you needing to extract the text first. upvoted 6 times

Selected Answer: A Amazon Comprehend can extract directly from PDF. upvoted 5 times

Selected Answer: A C is incorrect as Comprehend extracts entities FROM text, not "converts entities to text" upvoted 1 times

Selected Answer: A Comprehend strengths: Fully managed nlp service built-in entity extraction capability no training required immediate processing direct integration with S3 Can detect entities like: names, dates, organizations, locations, and custom entities upvoted 1 times

Selected Answer: C Correct answer is C. Comprehend can't directly extract from PDF as it extracts from texts. So, you need to use Textract to extract from PDF first and then use Comprehend. upvoted 2 times

Question 16 (Topic 1)

An ML engineer needs to deploy a trained model that is based on a genetic algorithm. The algorithm solves a complex problem and can take several minutes to generate predictions. When the model is deployed, the model needs to access large amounts of data to process requests. The requests can involve as much as 100 MB of data. Which deployment solution will meet these requirements with the LEAST operational overhead?

Select an option, then click Submit answer.

○ Deploy the model to Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer.
○ Deploy the model to an Amazon SageMaker real-time endpoint.
○ Deploy the model to an Amazon SageMaker Asynchronous Inference endpoint.
○ Package the model as a container. Deploy the model to Amazon Elastic Container Service (Amazon ECS) on Amazon EC2 instances.

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Question 17 (Topic 1)

An ML engineer wants to use a set of survey responses as training data for an ML classifier. All the survey responses are either “yes” or “no.” The ML engineer needs to convert the responses into a feature that will produce better model training results. The ML engineer must not increase the dimensionality of the dataset. Which methods will meet these requirements? (Choose two.)

Select an option, then click Submit answer.

○ Binary encoding
○ Label encoding
○ One-hot encoding
○ Statistical imputation
○ Tokenization

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Question 18 (Topic 1)

A company has significantly increased the amount of data that is stored as .csv files in an Amazon S3 bucket. Data transformation scripts and queries are now taking much longer than they used to take. An ML engineer must implement a solution to optimize the data for query performance. Which solution will meet this requirement with the LEAST operational overhead?

Select an option, then click Submit answer.

○ Configure an AWS Lambda function to split the .csv files into smaller objects in the S3 bucket.
○ Configure an AWS Glue job to drop columns that have string type values and to save the results to the S3 bucket.
○ Configure an AWS Glue extract, transform, and load (ETL) job to convert the .csv files to Apache Parquet format.
○ Configure an Amazon EMR cluster to process the data that is in the S3 bucket.

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Question 19 (Topic 1)

An ML engineer uses one ML framework to train multiple ML models. The ML engineer needs to optimize the inference costs and host the models on Amazon SageMaker AI. Which solution will meet these requirements MOST cost-effectively?

Select an option, then click Submit answer.

○ Create a multi-container inference endpoint for direct invocation.
○ Create a multi-model inference endpoint for all the models.
○ Create a multi-container inference endpoint for sequential invocation.
○ Create multiple single-model inference endpoint for each model.

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Question 20 (Topic 1)

A company collects customer data every day. The company stores the data as compressed files in an Amazon S3 bucket that is partitioned by date. Every month, analysts download the data, process the data to check the data quality, and then upload the data to Amazon QuickSight dashboards. An ML engineer needs to implement a solution to automatically check the data quality before the data is sent to QuickSight. Which solution will meet these requirements with the LEAST operational overhead?

Select an option, then click Submit answer.

○ Run an AWS Glue crawler every month to update the AWS Glue Data Catalog. Use AWS Glue Data Quality rules to check the data quality.
○ Use an AWS Glue trigger to run an AWS Glue crawler every month to update the AWS Glue Data Catalog. Create an AWS Glue job that loads the data into a PySpark DataFrame. Configure the job to apply custom functions and to evaluate the data quality.
○ Run Python scripts on an AWS Lambda function every month to evaluate data quality. Configure the S3 bucket to invoke the Lambda function when objects are added to the S3 bucket.
○ Configure the S3 bucket to send event notifications to an Amazon Simple Queue Service (Amazon SQS) queue when objects are uploaded. Use Amazon CloudWatch insights every month for the SQS queue to evaluate the data quality.

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Question 21 (Topic 1)

A company runs an Amazon SageMaker AI domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker AI domain. Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address. Which update to the network configuration will meet this requirement?

Select an option, then click Submit answer.

○ Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.
○ Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network ACL for the subnet where the domain is located.
○ Create a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.
○ Create a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.

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Question 22 (Topic 1)

An ML engineer is building a logistic regression model to predict customer churn for subscription services. The ML engineer is using a dataset that contains two string variables: location and job_seniority_level. The location variable has 3 distinct values, and the job_seniority_level variable has over 10 distinct values. The ML engineer must perform preprocessing on the variables. Which solution will meet this requirement?

Select an option, then click Submit answer.

○ Apply tokenization to location. Apply ordinal encoding to job_seniority_level.
○ Apply one-hot encoding to location. Apply ordinal encoding to job_seniority_level
○ Apply binning to location. Apply standard scaling to job_seniority_level.
○ Apply one-hot encoding to location. Apply standard scaling to job_seniority_level.

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Question 23 (Topic 1)

An ML engineer needs to develop an AI assistant by using Retrieval Augmented Generation (RAG) in Amazon Bedrock. The company stores a collection of PDF text files in an Amazon S3 bucket. The ML engineer must create an Amazon Bedrock knowledge base to process the PDF files and to store the processed files in a vector store. Which solution will meet these requirements?

Select an option, then click Submit answer.

○ Use the latest version of Amazon Titan Text Premier to perform document embedding.
○ Use the latest version of Mistral 7B Instruct to perform instruction tuning.
○ Use the latest version of Anthropic Claude Sonnet to perform instruction tuning.
○ Use Cohere Embed Multilingual to perform document embedding.

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Question 24 (Topic 1)

A digital media entertainment company needs real-time video content moderation to ensure compliance during live streaming events. Which solution will meet these requirements with the LEAST operational overhead?

Select an option, then click Submit answer.

○ Use Amazon Rekognition and AWS Lambda to extract and analyze the metadata from the videos’ image frames.
○ Use Amazon Rekognition and a large language model (LLM) hosted on Amazon Bedrock to extract and analyze the metadata from the videos’ image frames.
○ Use Amazon SageMaker AI to extract and analyze the metadata from the videos’ image frames.
○ Use Amazon Transcribe and Amazon Comprehend to extract and analyze the metadata from the videos’ image frames.

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About the AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 Free Practice Test

This page holds a free practice test for the AWS Certified Machine Learning Engineer - Associate MLA-C01 (AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01) certification from Amazon AWS. The 24 questions are a free sample of the full Study4Pass practice set of 271 questions, so you can check your level before you commit to the real exam. The free sample includes 22 single-answer and 2 interactive questions — the same mix of formats you will meet in the full set. The Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam is part of the AWS Certified Machine Learning Engineer - Associate MLA-C01 certification.

The target audience of the Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam is professionals who want to validate their skills in AWS Certified Machine Learning Engineer - Associate MLA-C01 and demonstrate their competency… The prerequisites of the Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam, if any, are defined by Amazon AWS in the official exam guide.

Certification: AWS Certified Machine Learning Engineer - Associate MLA-C01 (AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01)
Vendor: Amazon AWS
Free on this page: 24 practice questions with answers and explanations
Full practice set: 271 questions

Treat this sample as a diagnostic. Answer honestly, check the explanation after every question, and note which topics feel shaky. A short, focused review of those topics is worth more than re-reading everything — and the full set lets you drill them topic by topic.

A good way to use this page: take the whole sample in one sitting, then go back over every explanation with fresh eyes. Mark the topics that felt uncertain, not just the ones you got wrong — uncertain answers are unmarked risk on the real exam. One or two focused passes like this usually move a score more than days of passive reading.

Inside the full AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 practice set:

All 271 questions with answers and explanations
Timed Exam Simulation that mirrors the real exam pressure
Topic Training to drill one domain at a time
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How to use this AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 free practice test

Answer a question, then check the result to see the correct answer and the explanation behind it. Work at your own pace — there is no timer and no signup. Stuck on a concept? This is an AI-Enabled practice test: use Ask AI on any question and get the underlying concept explained in plain words, so you learn the reasoning instead of memorizing answers. Ready for more? The full 271-question set adds a timed Exam Simulation mode, topic training for your weak areas, and a flagged-question review, so you can practise the way the real exam feels.

What to study for each AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 domain

The AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 practice set is built to mirror the full AWS Certified Machine Learning Engineer - Associate MLA-C01 syllabus, so every major area of the real exam is represented in the questions. The Amazon AWS AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam is part of the AWS Certified Machine Learning Engineer - Associate MLA-C01 certification. The most reliable study method with a set like this is simple: practise a section, read the explanations, then go back to the official Amazon AWS documentation for any topic you missed, and repeat until the explanations feel obvious.

AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 practice test — quick questions

Is this AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 practice test free?

Yes — the 24 questions on this page are completely free, with no signup and no payment details needed.

How many questions are in the full AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 set?

271 questions. The 24 free questions above are a sample of that full set, in the same style and topic balance.

Does the free test show the answers?

Yes — every question shows the correct answer with an explanation, and Ask AI can explain the concept behind a question in plain words if you want to go deeper.

Is this the real AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam?

No. This is a practice test: the questions are written to mirror the real AWS-CERTIFIED-MACHINE-LEARNING-ENGINEER-ASSOCIATE-MLA-C01 exam’s topics and difficulty, not copied from it.