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AWS Machine Learning Fundamentals Flashcards
AWS Machine Learning Engineer Associate MLA-C01 Flashcards
Study our AWS Machine Learning Fundamentals flashcards for the AWS Machine Learning Engineer Associate MLA-C01 exam with 40+ flashcards. View as flashcards, a searchable table, or as a fun matching game.
| Front | Back |
| How does AWS Glue support machine learning | Provides tools for data preparation and integration for ML workflows |
| How does SageMaker Automatic Model Tuning work | Uses Bayesian optimization to find the best hyperparameters for training jobs |
| How does SageMaker Debugger assist | Performs real-time debugging of training jobs |
| How does SageMaker Feature Store help | Provides a centralized repository for storing, sharing, and managing machine learning features |
| How does SageMaker Ground Truth help | Simplifies data labeling using AI-assisted workflows |
| What are AWS Inferentia chips | Dedicated hardware designed to accelerate machine learning inference tasks |
| What does Amazon Lex enable | Builds conversational interfaces like chatbots powered by machine learning |
| What does Amazon Personalize do | Provides recommendations for users based on machine learning algorithms |
| What does Amazon Rekognition do | Provides image and video analysis powered by machine learning |
| What does Amazon Textract do | Extracts text and data from documents using machine learning |
| What does SageMaker Model Monitor do | Detects and reports anomalies in models deployed to production |
| What does SageMaker Studio provide | A fully integrated development environment for machine learning |
| What is a benefit of SageMaker | Simplifies the machine learning workflow including data preparation and model deployment |
| What is a benefit of SageMaker Clarify | Provides insights into model bias and explainability |
| What is a feature of SageMaker Autopilot | Automatically generates candidate models with pre-tuned parameters |
| What is a key feature of SageMaker Neo | Optimizes models to run faster on edge devices |
| What is Amazon Augmented AI (A2I) | Enables human review of machine learning predictions for sensitive use cases |
| What is Amazon Comprehend | A natural language processing service that provides insights like sentiment analysis and entity recognition |
| What is Amazon Kendra | An intelligent search service powered by machine learning for enterprise solutions |
| What is Amazon SageMaker | A managed service for building, training, and deploying machine learning models at scale |
| What is Amazon Translate | Provides language translation services powered by machine learning |
| What is an endpoint in SageMaker | A resource for deploying and hosting machine learning models |
| What is AWS AI Services | Pre-trained AI tools for tasks such as vision, language, recommendations, and forecasting |
| What is AWS Deep Learning AMI | Preconfigured EC2 instances for deep learning tasks |
| What is AWS DeepRacer | A cloud-based platform for applying reinforcement learning to race autonomous vehicles |
| What is SageMaker Autopilot | A tool that automates the machine learning model selection, training, and tuning process |
| What is SageMaker Distributed Training | Enables faster training of deep learning models across multiple GPUs and nodes |
| What is SageMaker Experiments used for | Organizing and tracking machine learning model training runs and metadata |
| What is SageMaker JumpStart | Offers ready-made machine learning solutions and pre-trained models |
| What is SageMaker Pipeline | A tool for automating end-to-end machine learning workflows |
| What is SageMaker Profiler | Analyzes resource utilization and bottlenecks during training jobs to optimize performance |
| What is SageMaker Studio Lab | A free service for experimenting with machine learning in a collaborative environment |
| What is the function of SageMaker Autodoc | Automatically generates documentation for machine learning workflows |
| What is the purpose of Amazon Forecast | Provides time-series forecasting using machine learning models |
| What is the purpose of SageMaker Data Wrangler | Simplifies the process of data preparation and feature engineering |
| What is the purpose of SageMaker Model Registry | Helps catalog, manage, and deploy ML models consistently |
| What ML algorithms does SageMaker provide | Prebuilt algorithms and tools for custom algorithm creation |
| What type of analysis does Amazon Rekognition perform | Face recognition, object detection, and scene analysis |
| What type of service is Amazon Polly | Converts text into lifelike speech using machine learning |
| What type of service is Amazon Rekognition | Image and video recognition service |
About the Flashcards
Flashcards for the AWS Machine Learning Engineer Associate exam provide a concise study tool for mastering AWS machine learning essentials. Quickly recall what Amazon SageMaker offers for building, training, tuning, and deploying models, and understand how services such as Rekognition or Comprehend add pre-trained intelligence to applications.
Each card reinforces key terminology, benefits, and workflows-from Ground Truth data labeling, Autopilot model selection, Feature Store management, and Model Monitor oversight to Forecast time-series predictions, Personalize recommendations, and Translate language support. Hardware accelerators like Inferentia and specialty options like DeepRacer are also covered, ensuring you can match services to use cases and explain their value in real-world scenarios.
Topics covered in this flashcard deck:
- Amazon SageMaker platform
- ML workflow automation
- AI vision services
- NLP & speech services
- Forecasting and recommendations