Amazon AI Practitioner Final review
Exam Preparation Guide: AWS Services and Machine Learning Concepts
Introduction
Welcome to this comprehensive guide designed to help you ace your exam! This blog post covers key AWS services and machine learning concepts, breaking them down into digestible sections with clear explanations and examples. Whether you’re studying for a certification or brushing up on technical knowledge, this guide will equip you with the essentials. Let’s dive in!
- AWS Services: Governance and Compliance
AWS Config
Purpose: AWS Config enables you to assess, audit, and evaluate your AWS resource configurations.
How It Works: Continuously monitors and records resource configurations, allowing automated compliance checks against desired settings.
Why It Matters: Crucial for AI system governance, ensuring resources align with organizational policies and regulatory standards.
Example: A company uses AWS Config to ensure all S3 buckets enforce encryption, flagging non-compliant resources automatically.
Incorrect Options:
Amazon Inspector: Focuses on security assessments for applications, not continuous config monitoring.
AWS Audit Manager: Simplifies risk and compliance audits but lacks config monitoring.
AWS Artifact: Provides compliance reports on-demand, not real-time monitoring.
AWS Trusted Advisor
Purpose: Offers guidance to optimize your AWS resources based on best practices.
How It Works: Analyzes your environment for cost savings, performance, security, and fault tolerance improvements.
Why It Matters: Essential for AI governance by ensuring an optimized and secure setup.
Example: Trusted Advisor flags an underutilized EC2 instance, saving costs.
Incorrect Options:
AWS Config: Monitors configs, not optimization.
AWS Audit Manager: Focuses on compliance audits.
AWS CloudTrail: Logs API calls for auditing, not optimization advice.
- Amazon Kendra
Purpose: A machine learning-powered enterprise search service.
How It Works: Enables developers to add search capabilities to apps, helping users find info across diverse data sources.
Supported Sources: Manuals, FAQs, HR docs, and more from S3, SharePoint, Salesforce, ServiceNow, RDS, and OneDrive.
Example: A support team uses Kendra to quickly search customer service guides stored in S3 and Salesforce.
Incorrect Options:
Amazon Textract: Extracts text from documents, not a search tool.
SageMaker Data Wrangler: Prepares data for ML, not search-focused.
Amazon Comprehend: Analyzes text for insights, not document search.
- OpenSearch Serverless Vector Store
Purpose: Powers Knowledge Bases for Amazon Bedrock by managing document ingestion.
How It Works: Converts documents into embeddings (vectors) and stores them in a vector database.
Supported Databases: OpenSearch Serverless, Pinecone, Redis Enterprise Cloud, Aurora, MongoDB.
Example: A company uploads PDFs to Bedrock, which OpenSearch Serverless indexes as vectors for fast retrieval.
- Machine Learning Concepts
CNNs and RNNs
CNNs (Convolutional Neural Networks):
Best for grid-like data (e.g., images).
Learns spatial feature hierarchies automatically.
Example: Identifying objects in photos.
RNNs (Recurrent Neural Networks):
Best for sequential data (e.g., time-series).
Captures temporal dependencies.
Example: Predicting stock prices.
K-Means vs. KNN
K-Means:
Unsupervised clustering algorithm.
Groups data by minimizing within-cluster variance.
Example: Segmenting customers by purchase behavior.
KNN (K-Nearest Neighbors):
Supervised classification algorithm.
Classifies based on k-nearest labeled neighbors.
Example: Classifying emails as spam or not.
- Evaluation Metrics
Precision: Accuracy of positive predictions = True Positives / (True Positives + False Positives).
Recall: Ability to find all positives = True Positives / (True Positives + False Negatives).
F1-Score: Harmonic mean of Precision and Recall, balancing both.
Example: In spam detection, Precision ensures flagged emails are truly spam, Recall ensures no spam is missed, and F1 balances the two.
- Top K, Temperature, and Top P
Top K: Number of top candidates for the next token. Lower = more focused, higher = more diverse.
Temperature: Controls creativity (0-1). Lower = deterministic, higher = creative.
Top P: Percentage of top candidates considered. Similar to Top K in effect.
Example: For a chatbot, low Temperature ensures predictable replies, while high Top K adds variety.
- Learning Types
Self-Supervised Learning: Creates labels from input data (used by foundation models).
Supervised Learning: Trains with labeled data.
Fine-Tuning: Further trains a pre-trained model with labeled data for specific tasks.
Example: A foundation model learns language patterns self-supervised, then fine-tunes on customer reviews.
- Prompting Techniques
Key Components:
Instructions: Task for the model.
Context: Guiding info.
Input Data: What to respond to.
Output Indicator: Desired format.
Example: “Summarize this article (context) in 3 sentences (instruction) for this text (input) as a bullet list (output).”
- Bias and Variance
Bias: High in underfit models, missing data patterns.
Variance: High in overfit models, overly sensitive to training data.
Example: A simple model (high bias) fails to predict complex trends, while an overfit model (high variance) memorizes noise.
- Continued Pre-Training vs. Fine-Tuning
Continued Pre-Training: Uses unlabeled data to enhance domain knowledge.
Fine-Tuning: Uses labeled data to boost task-specific performance.
Example: Pre-train on business docs (unlabeled), then fine-tune on labeled sales data.
- Interpretability vs. Performance Trade-Off
Interpretability: Simpler models (e.g., linear regression) are easier to understand but less powerful.
Performance: Complex models (e.g., neural networks) excel but are harder to interpret.
Example: A decision tree is interpretable but may underperform compared to a deep learning model.
- Interpretability vs. Explainability
Interpretability: Understanding a model’s internal mechanics.
Explainability: Providing clear reasons for predictions, especially for complex models.
Example: A linear model’s weights are interpretable; a neural network’s predictions need explainability tools like SHAP.