AWS Certified AI Practitioner AIF-C01 Full Course Free

The requirement is to deploy a customized machine learning (ML) model and monitor its quality for potential drift over time in a production environment. Let’s evaluate each option:

A. Amazon Rekognition: This service is designed for image and video analysis, such as object detection, facial recognition, and text extraction. It is not suited for deploying custom ML models or monitoring model quality drift.

B. Amazon SageMaker Clarify: This feature helps detect bias in ML models and explains model predictions. While it addresses fairness and interpretability, it does not specifically focus on monitoring model quality drift over time in production.

C. Amazon Comprehend: This is a natural language processing (NLP) service for extracting insights from text, such as sentiment analysis or entity recognition. It does not support deploying custom ML models or monitoring model performance drift.

D. Amazon SageMaker Model Monitor: This feature is part of Amazon SageMaker and is specifically designed to monitor ML models in production. It tracks metrics such as data drift, model drift, and performance degradation over time, alerting users when issues are detected.

Exact Extract Reference: According to the AWS documentation on Amazon SageMaker, “Amazon SageMaker Model Monitor allows you to detect and remediate data and model quality issues in production. It continuously monitors the performance of deployed models, capturing data and model predictions to detect deviations from expected behavior, such as data drift or model performance degradation.” (Source: AWS SageMaker Documentation - Model Monitoring, ).

This directly aligns with the requirement to observe model quality drift, making Amazon SageMaker Model Monitor the correct choice.

The problem involves a simple probability calculation that can be handled efficiently by straightforward mathematical rules and computations. Using machine learning techniques would introduce unnecessary complexity and operational overhead.

Option C (Correct): "Use code that will calculate probability by using simple rules and computations": This is the correct answer because it directly solves the problem with minimal overhead, using basic probability rules.

Option A: "Use supervised learning to create a regression model" is incorrect as it overcomplicates the solution for a simple probability problem.

Option B: "Use reinforcement learning to train a model" is incorrect because reinforcement learning is not needed for a simple probability calculation.

Option D: "Use unsupervised learning to create a model" is incorrect as unsupervised learning is not applicable to this task.

AWS AI Practitioner References:

Choosing the Right Solution for AI Tasks: AWS recommends using the simplest and most efficient approach to solve a given problem, avoiding unnecessary machine learning techniques for straightforward tasks.

RAG combines large pre-trained models with retrieval mechanisms to fetch relevant context from a knowledge base. This approach is cost-effective as it eliminates the need for frequent model retraining while ensuring responses are contextually accurate and up to date. References: AWS RAG Techniques.

The company is developing ML applications for various use cases, and the task is to select the correct ML paradigm (supervised or unsupervised learning) for each. Supervised learning involves training a model on labeled data to make predictions, while unsupervised learning identifies patterns or structures in unlabeled data. Each use case aligns with one of these paradigms based on its requirements.

Exact Extract from AWS AI Documents:

From the AWS AI Practitioner Learning Path:

"Supervised learning uses labeled data to train models for tasks like classification (e.g., binary or multi-class classification), where the model predicts a category. Unsupervised learning works with unlabeled data for tasks like clustering (e.g., K-means clustering) or dimensionality reduction, identifying patternsor reducing data complexity without predefined labels."

(Source: AWS AI Practitioner Learning Path, Module on Machine Learning Strategies)

Detailed Explanation:

Binary classification: Supervised learningBinary classification involves predicting one of two classes (e.g., yes/no, spam/not spam) using labeled data, making it a supervised learning task. The model learns from examples where the correct class is provided.

Multi-class classification: Supervised learningMulti-class classification extends binary classification to predict one of multiple classes (e.g., categorizing items into several groups). Like binary classification, it requires labeled data, so it falls under supervised learning.

K-means clustering: Unsupervised learningK-means clustering groups data into clusters based on similarity, without requiring labeled data. This is a classic unsupervised learning task, as the algorithm identifies patterns in the data on its own.

Dimensionality reduction: Unsupervised learningDimensionality reduction (e.g., using techniques like PCA) reduces the number of features in a dataset while preserving important information. It does not require labeled data, making it an unsupervised learning task.

Hotspot Selection Analysis:

The hotspot lists four use cases, each with a dropdown containing "Select...," "Supervised learning," and "Unsupervised learning." The correct selections are:

Binary classification: Supervised learning

Multi-class classification: Supervised learning

K-means clustering: Unsupervised learning

Dimensionality reduction: Unsupervised learning

Each paradigm (supervised and unsupervised learning) is used twice, as the question allows for paradigms to be selected one or more times.