AWS Certified Machine Learning Engineer - Associate Practice Questions: ML Model Development Domain
Test your AWS Certified Machine Learning Engineer - Associate knowledge with 10 practice questions from the ML Model Development domain. Includes detailed explanations and answers.
AWS Certified Machine Learning Engineer - Associate Practice Questions
Master the ML Model Development Domain
Test your knowledge in the ML Model Development domain with these 10 practice questions. Each question is designed to help you prepare for the AWS Certified Machine Learning Engineer - Associate certification exam with detailed explanations to reinforce your learning.
Question 1
You are tasked with deploying a machine learning model using AWS SageMaker. The model needs to handle real-time predictions and scale automatically based on demand. Which SageMaker feature should you use to achieve this?
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Correct Answer: B
Explanation: SageMaker Hosting Services with Auto Scaling is designed for deploying models for real-time predictions. It allows you to configure automatic scaling policies to handle varying levels of demand. Batch Transform is used for batch processing, Ground Truth is for data labeling, and Processing is for running data processing jobs.
Question 2
You have a trained machine learning model that you want to deploy on an edge device. Which AWS service can help you optimize the model for this purpose?
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Correct Answer: B
Explanation: Option B is correct because Amazon SageMaker Neo optimizes machine learning models to run efficiently on various edge devices. Option A is used for running AWS Lambda functions on edge devices. Option C is for serverless compute, not specifically for edge optimization. Option D is for real-time data streaming.
Question 3
You are tasked with deploying a machine learning model for real-time inference on AWS. Which service provides the most efficient and scalable solution for this requirement?
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Correct Answer: B
Explanation: Amazon SageMaker Endpoints are specifically designed for deploying machine learning models for real-time inference. They provide a scalable and efficient solution for serving models with low latency. Option A, Amazon API Gateway, is used for creating and managing APIs but not for model deployment. Option C, Amazon S3, is a storage service and not suitable for real-time inference. Option D, Amazon RDS, is a relational database service and not intended for serving ML models.
Question 4
You have a dataset with missing values and need to prepare it for training a machine learning model on AWS. Which AWS service can help you automate the data cleaning process?
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Correct Answer: A
Explanation: Amazon SageMaker Data Wrangler provides an easy-to-use interface for data preparation tasks, including cleaning and transforming data, which makes it ideal for handling missing values. Amazon Redshift (B) is a data warehousing service, AWS Glue (C) is an ETL service, and Amazon QuickSight (D) is a business intelligence tool.
Question 5
You need to preprocess a large dataset stored in Amazon S3 for training a machine learning model. Which AWS service would you use to efficiently transform and prepare the data?
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Correct Answer: A
Explanation: AWS Glue is a fully managed ETL service that makes it easy to prepare and transform data for analytics and machine learning. It can efficiently process data stored in Amazon S3. Amazon Athena is used for querying data, Amazon EMR is for big data processing, and Amazon RDS is a relational database service.
Question 6
To monitor the performance of a deployed model in real-time on AWS SageMaker, which feature should you use?
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Correct Answer: A
Explanation: Option A is correct because SageMaker Model Monitor provides real-time monitoring of deployed models, including data drift detection. Option B can be used for logging but does not provide ML-specific monitoring. Option C is for debugging model training jobs. Option D is for tracing distributed applications.
Question 7
You are tasked with integrating a pre-trained language model into your application using AWS services. Which service would you use to deploy the model for inference without needing to manage the underlying infrastructure?
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Correct Answer: C
Explanation: Amazon Bedrock is designed for deploying pre-trained models for inference without the need to manage infrastructure. SageMaker can also be used but requires more setup. Lambda is not suitable for large model inference due to resource limitations, and EC2 requires you to manage the infrastructure.
Question 8
You are planning to use AWS SageMaker for an end-to-end machine learning workflow, including data preparation, model training, and deployment. Which SageMaker feature allows you to orchestrate these steps in a repeatable and automated manner?
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Correct Answer: A
Explanation: SageMaker Pipelines is designed for building, managing, and automating end-to-end machine learning workflows. It allows you to create pipelines that include steps for data preparation, training, and deployment. Ground Truth is for data labeling, Neo is for model optimization, and Clarify is for bias detection and explainability.
Question 9
You have deployed a model on SageMaker and need to monitor its performance in real-time. Which AWS service can you use to visualize the model's predictions and performance metrics?
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Correct Answer: A
Explanation: AWS CloudWatch is a monitoring and observability service that can be used to collect and track metrics, collect and monitor log files, and set alarms. It is suitable for monitoring the performance of models deployed on SageMaker. Amazon QuickSight (C) is mainly used for business intelligence and data visualization, not direct monitoring. AWS Lambda (B) is for running code in response to events, and AWS Glue (D) is for ETL operations.
Question 10
You need to preprocess a large dataset for your machine learning model training on AWS. The dataset is stored in Amazon S3, and you want to perform distributed data processing. Which AWS service is most suitable for this task?
Show Answer & Explanation
Correct Answer: A
Explanation: Amazon EMR (Elastic MapReduce) is an AWS service that provides a managed Hadoop framework to process large datasets across dynamically scalable Amazon EC2 instances. It is ideal for distributed data processing tasks. Option B, AWS Lambda, is not suitable for large-scale data processing due to its execution time limits and resource constraints. Option C, Amazon RDS, is a relational database service and not meant for distributed processing. Option D, AWS Batch, is used for batch computing jobs but is not specifically optimized for distributed data processing like EMR.
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About AWS Certified Machine Learning Engineer - Associate Certification
The AWS Certified Machine Learning Engineer - Associate certification validates your expertise in ml model development and other critical domains. Our comprehensive practice questions are carefully crafted to mirror the actual exam experience and help you identify knowledge gaps before test day.