AWS AI Practitioner flashcards

32 cards. Read the term and the definition here. The deck at the top of this page is optional.

Artificial intelligence

The broad discipline of building systems that perform tasks requiring human-like intelligence, such as reasoning, perception, and language understanding. Machine learning is one approach within it.

Machine learning

A method where algorithms learn patterns and decision rules from data rather than following hand-coded logic, then generalize those patterns to new, unseen inputs.

Supervised learning

Training on labeled examples where each input has a known target. Classification predicts discrete categories such as spam or not spam; regression predicts continuous numeric values.

Unsupervised learning

Training on unlabeled data to discover inherent structure, such as grouping similar records through clustering or reducing dimensions while preserving important variance.

Overfitting

A model fits training data noise so closely that it performs well on training data but poorly on new data, showing low training error and high validation error.

Validation versus test set

The validation set guides hyperparameter tuning during development; the test set is held out and used once at the end to estimate real-world generalization.

Amazon SageMaker

AWS managed platform covering the ML lifecycle: data labeling, notebook experimentation, distributed training, hyperparameter tuning, model registry, and endpoint deployment.

Batch transform

A SageMaker inference mode for scoring large datasets offline in a single job, suited to non-interactive workloads where per-request latency is not a concern.

Foundation model

A very large model pretrained on broad, general data that can be adapted through prompting or fine-tuning to many downstream tasks without training from scratch.

Transformer self-attention

A mechanism that lets each token weigh its relevance to every other token in the sequence in parallel, capturing long-range context and enabling scalable training.

Token and context window

A token is a subword text unit the model processes; the context window is the maximum combined tokens of input and output allowed in one request.

Temperature

A sampling parameter controlling output randomness. Lower values make responses more deterministic and focused; higher values increase diversity and creative variation.

Top-p sampling

Nucleus sampling that restricts token selection to the smallest set whose cumulative probability reaches p, balancing coherence against variety in generated text.

Embeddings

Dense numeric vectors representing text or images so that semantically similar items sit close together, measured by cosine similarity and used for search.

Hallucination

Model output that is fluent and confident but factually unsupported or fabricated. Retrieval grounding, citations, and guardrails reduce it but cannot fully eliminate it.

Retrieval-augmented generation

A pattern that retrieves relevant documents from a knowledge store and inserts them into the prompt so the model answers from supplied evidence rather than memory alone.

Few-shot prompting

Including several worked input-output examples in the prompt so the model infers the desired task format and style without any weight updates.

Chain-of-thought prompting

Asking the model to reason through intermediate steps before producing a final answer, which improves accuracy on multi-step arithmetic and logic tasks.

Fine-tuning versus RAG

Fine-tuning adjusts model weights to change style or behavior; retrieval-augmented generation supplies external, updatable facts at inference without retraining the model.

Amazon Bedrock Knowledge Bases

A managed RAG capability that chunks and embeds documents into a vector store, then retrieves relevant passages to ground model responses.

Bedrock Agents

A managed feature that lets a foundation model plan multi-step tasks and call external APIs defined as action groups to fulfill user requests.

Provisioned throughput

A Bedrock purchasing option reserving dedicated model capacity for predictable, high-volume workloads, offering steadier latency than on-demand pay-per-token pricing.

Bedrock Guardrails

Configurable controls that filter harmful content, deny specific topics, mask personally identifiable information, and ground responses to reduce unsafe or unsupported output.

SageMaker Clarify

A tool that detects potential bias in datasets and trained models and generates feature-attribution explanations showing which inputs drove predictions.

SageMaker Model Monitor

A production monitoring capability that detects data drift, model quality drift, and bias drift by comparing live inference data against a captured baseline.

Human-in-the-loop review

A workflow routing low-confidence or high-stakes AI outputs to people for verification, using services such as Amazon Augmented AI before actions are finalized.

Model card

A transparency document describing a model's intended use, training data, performance metrics, and known limitations so downstream users can assess suitability.

Shared responsibility model

AWS secures infrastructure and managed service internals; customers remain responsible for their data, identity permissions, encryption configuration, and application code.

Least privilege for AI workloads

Granting each model or application role only the specific actions and resources it needs, such as scoped Bedrock invoke permissions, instead of broad wildcard access.

VPC endpoint for Bedrock

A PrivateLink interface endpoint that keeps model invocation traffic inside the AWS network, avoiding exposure over the public internet.

Amazon Macie

A managed data security service that uses pattern and machine learning detection to discover and classify sensitive data such as credentials in S3.

Prompt injection

An attack where crafted input causes a model to ignore its instructions or leak data; mitigations include input validation, output sanitization, and least-privilege tool access.