Related Certification(s):
Amazon Associate Certification
Amazon MLA-C01 Exam Topics - You’ll Be Tested in Actual Exam
The Amazon MLA-C01 exam is a comprehensive assessment designed to evaluate your knowledge and skills in working with Amazon Web Services (AWS). It covers a wide range of topics, ensuring that you have a solid understanding of the AWS platform and its various services. The exam topics include understanding the AWS global infrastructure, its core services like EC2, S3, and RDS, and how to design and implement secure and scalable solutions. You'll also need to grasp the concepts of networking, storage, and database services on AWS. Additionally, the exam delves into identity and access management, security best practices, and how to ensure data privacy and compliance. Another crucial aspect is learning how to monitor and troubleshoot AWS resources effectively. The exam also assesses your ability to design cost-effective solutions, optimize resource utilization, and implement billing and cost management strategies. Furthermore, it covers the basics of working with AWS Lambda, a serverless computing service, and how to utilize AWS tools for application development and deployment. Lastly, you'll need to familiarize yourself with the AWS Well-Architected Framework and its pillars, which provide a systematic approach to building and operating reliable, secure, efficient, and sustainable applications on AWS.
Amazon MLA-C01 Exam Short Quiz
Attempt this Amazon MLA-C01 exam quiz to self-assess your preparation for the actual Amazon AWS Certified Machine Learning Engineer - Associate exam. CertBoosters also provides premium Amazon MLA-C01 exam questions to pass the Amazon AWS Certified Machine Learning Engineer - Associate exam in the shortest possible time. Be sure to try our free practice exam software for the Amazon MLA-C01 exam.
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Amazon MLA-C01 Exam Quiz
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AmazonMLA-C01
Q1:
An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured dat
a. 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?
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AConfigure IAM policies on an AWS Glue Data Catalog to restrict access to Athena based on the ML engineers' campaigns.
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BStore users and campaign information in an Amazon DynamoDB table. Configure DynamoDB Streams to invoke an AWS Lambda function to update S3 bucket policies.
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CUse Lake Formation to authorize AWS Glue to access the S3 bucket. Configure Lake Formation tags to map ML engineers to their campaigns.
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DConfigure S3 bucket policies to restrict access to the S3 bucket based on the ML engineers' campaigns.
AmazonMLA-C01
Q2:
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine.
The model is using sensitive dat
a. An ML engineer needs to implement a solution to identify and remove the sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?
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ADeploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.
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BDeploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Create an AWS Batch job to identify and remove the sensitive data.
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CUse Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.
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DUse Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.
AmazonMLA-C01
Q3:
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 the existing 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?
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ACreate a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.
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BCreate a model group for each category. Move the existing models into these category model groups.
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CUse SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
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DCreate a Model Registry collection for each of the three categories. Move the existing model groups into the collections.
AmazonMLA-C01
Q4:
A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the new model's performance by using live data and without affecting production end users.
Which solution will meet these requirements?
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ASet up SageMaker Debugger and create a custom rule.
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BSet up blue/green deployments with all-at-once traffic shifting.
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CSet up blue/green deployments with canary traffic shifting.
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DSet up shadow testing with a shadow variant of the new model.
AmazonMLA-C01
Q5:
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?
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AUse SageMaker built-in algorithms to train the proprietary datasets.
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BUse SageMaker script mode and premade images for ML frameworks.
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CBuild a container on AWS that includes custom packages and a choice of ML frameworks.
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DPurchase similar production models through AWS Marketplace.
