AWS Certified Machine Learning Engineer - Associate (AWS-MLAE) Practice Questions: ML Operations and MLOps Domain

Correct Answer: A

Explanation: Endpoint Autoscaling in Amazon SageMaker allows you to automatically scale the number of instances for a deployed model endpoint based on the incoming traffic. Elastic Load Balancing distributes incoming application traffic, Auto Scaling Groups are used with EC2 instances, and AWS Lambda is for serverless compute.

Correct Answer: B

Explanation: Factorization Machines is a built-in algorithm in SageMaker that is well-suited for recommendation systems due to its ability to model interactions between features. Linear Learner is used for linear regression and classification tasks, DeepAR is for time series forecasting, and BlazingText is for natural language processing tasks.

Correct Answer: B

Explanation: AWS CodePipeline can be used to automate the build, test, and deploy phases of your release process. AWS CodeBuild can be used to build and test your model. Amazon SageMaker can be used to train and deploy your model. This combination allows you to create a CI/CD pipeline specifically for machine learning models. AWS Lambda and AWS Step Functions are more suited for orchestrating serverless workflows, not specifically for CI/CD pipelines.

Correct Answer: A

Explanation: Amazon SageMaker Clarify helps detect bias in machine learning models and datasets, providing insights into how to mitigate it. AWS Trusted Advisor offers best practices for AWS accounts, GuardDuty is for threat detection, and Security Hub provides a comprehensive view of security alerts.

Correct Answer: B

Explanation: In Amazon SageMaker, you can set a stopping condition for a training job to automatically stop it after a certain duration or if it exceeds a specified cost. AWS Budgets and CloudWatch alerts can notify you of cost overruns but cannot automatically stop a training job. AWS Config is used for compliance and monitoring configuration changes.

Correct Answer: B

Explanation: Option B is correct because using SageMaker's built-in distributed training with multiple ml.p3.16xlarge instances can significantly reduce training time due to the high computational power and GPU support. Option A would be much slower due to limited resources. Option C is inefficient and could lead to storage issues. Option D, while cost-effective, would not provide the computational power needed for large-scale deep learning tasks.

Correct Answer: B

Explanation: Amazon SageMaker is a fully managed service that allows developers and data scientists to build, train, and deploy machine learning models quickly and efficiently. Amazon Textract, Comprehend, and Rekognition are specialized AI services for document processing, natural language processing, and image analysis, respectively.

Correct Answer: C

Explanation: To efficiently utilize multiple GPUs, you should enable data parallelism using SageMaker's distributed training libraries, which are optimized for deep learning frameworks like TensorFlow and PyTorch. Using a powerful instance type like ml.p3.16xlarge provides multiple GPUs, but you need to configure your training script to use them effectively. Built-in algorithms and single GPU instances are not suitable for leveraging multiple GPUs.

Correct Answer: D

Explanation: SageMaker Automatic Model Tuning, also known as hyperparameter optimization, helps automate the process of tuning hyperparameters by running multiple training jobs to find the most effective combination. SageMaker Neo (A) is used for model optimization for deployment, SageMaker Autopilot (B) automates the end-to-end machine learning workflow, and SageMaker Ground Truth (C) is used for creating labeled datasets.

Correct Answer: C

Explanation: Over-sampling is a technique used to balance the class distribution in a dataset by increasing the number of instances in the minority class. Data Augmentation is used to increase the diversity of the training set, Feature Scaling adjusts the scale of features, and Dimensionality Reduction reduces the number of random variables.