AWS Certified Generative AI Developer - Professional: key terms

Flashcards for this exam AWS Certified Generative AI Developer - Professional guide

• Agent MemoryMechanisms that let an agent retain information within a task (short-term) and across sessions (long-term).Intermediate
• Agentic AIAI systems that act with autonomy to plan and execute multi-step tasks toward goals, rather than only responding to single prompts.Foundational
• Agentic RAGRAG in which an agent decides when, where and how to retrieve, possibly over multiple iterations and sources.Advanced
• Amazon BedrockAWS's fully managed service for building generative AI applications with foundation models through a single API.Foundational
• Amazon Bedrock AgentCoreAWS services for deploying and operating AI agents securely at scale with any framework or model.Advanced
• Amazon Bedrock GuardrailsConfigurable safeguards for Bedrock and other model applications: content filters, denied topics, PII redaction, grounding checks and more.Intermediate
• Amazon Bedrock Knowledge BasesA managed Bedrock feature that implements RAG: ingestion, chunking, embedding, vector storage and retrieval.Intermediate
• Amazon QAWS's generative AI assistant family: Amazon Q Business for enterprise knowledge and Amazon Q Developer for software development.Foundational
• Amazon SageMaker AIAWS's fully managed service to build, train, tune, deploy and monitor machine learning models.Foundational
• Amazon SageMaker ClarifyA SageMaker AI capability for detecting bias in data and models and explaining predictions.Intermediate
• Amazon SageMaker JumpStartA SageMaker AI hub of pretrained and foundation models and solutions that can be deployed and fine-tuned in your account.Intermediate
• Approximate Nearest Neighbor (ANN)Similarity search that returns near-closest vectors quickly by accepting a small loss in accuracy.Intermediate
• Base ModelA pretrained model before instruction tuning or alignment, which continues text rather than following instructions.Intermediate
• Batch InferenceGenerating predictions for a large dataset offline in a single job rather than per request.Foundational
• BERTScoreA text-similarity metric that compares contextual embeddings of generated and reference text.Advanced
• BLEUBilingual Evaluation Understudy: a metric scoring generated text by n-gram precision overlap with reference text.Intermediate
• BM25Best Matching 25: a standard lexical ranking function based on term frequency and inverse document frequency.Intermediate
• Catastrophic ForgettingWhen fine-tuning on new data causes a model to lose previously learned capabilities.Advanced
• ChunkingSplitting documents into smaller passages before embedding them for retrieval.Intermediate
• CitationsReferences in a model's answer that link claims to the specific source passages that support them.Intermediate
• ClaudeAnthropic's family of large language models, available in Opus, Sonnet and Haiku tiers.Foundational
• Content FilteringAutomatically detecting and blocking harmful categories of content in AI inputs and outputs.Foundational
• Contextual RetrievalPrepending chunk-specific context, generated from the whole document, to each chunk before embedding and indexing.Advanced
• Continued PretrainingFurther self-supervised training of a foundation model on large unlabeled domain data.Advanced
• Cosine SimilarityA similarity measure based on the angle between two vectors, ranging from -1 to 1.Intermediate
• Cross-EncoderA model that scores a query and document together in one pass, giving accurate relevance at higher cost.Advanced
• Data Masking for AIRedacting or replacing sensitive data such as PII before it reaches an AI model or appears in outputs.Intermediate
• Data PoisoningAn attack that corrupts training, fine-tuning or retrieval data to manipulate model behavior or implant backdoors.Intermediate
• Dot ProductThe sum of element-wise products of two vectors, used as a similarity score in retrieval and attention.Intermediate
• EmbeddingA dense numeric vector representing the meaning of text, images or other data so that similar items are close together.Foundational
• Embedding ModelA model specialized in converting inputs into embedding vectors for search and similarity tasks.Intermediate
• Evaluation HarnessA framework that runs a model or application over test cases and computes metrics automatically and repeatably.Advanced
• Excessive AgencyAn LLM risk where an agent has more functionality, permissions or autonomy than needed, enabling damaging actions.Intermediate
• Fine-TuningFurther training a pretrained model on a smaller task- or domain-specific dataset to adapt its behavior.Intermediate
• Foundation Model (FM)A large model pretrained on broad data that can be adapted to many downstream tasks.Foundational
• Full Fine-TuningUpdating all of a model's weights during fine-tuning.Intermediate
• Function CallingAn API feature where a model outputs a structured request to call a developer-defined function with arguments.Foundational
• Generative AI (GenAI)AI that creates new content such as text, images, code, audio or video based on patterns learned from training data.Foundational
• Golden DatasetA curated set of inputs with verified expected outputs used as the reference for evaluation and regression testing.Intermediate
• GraphRAGRAG that uses a knowledge graph of entities and relationships to retrieve connected information.Advanced
• GroundednessAn evaluation measure of whether a response's claims are supported by the provided context.Intermediate
• Grounding (Generative AI)Anchoring a model's responses to verifiable source data supplied at runtime.Foundational
• GuardrailsControls that constrain AI inputs and outputs to keep behavior safe, on-topic and compliant.Foundational
• HallucinationWhen a generative model produces fluent output that is false, fabricated or unsupported by its sources.Foundational
• HNSWHierarchical Navigable Small World: a graph-based ANN index offering fast, high-recall vector search.Advanced
• Human EvaluationAssessment of model outputs by human raters, often treated as the gold standard for quality.Foundational
• Hybrid SearchCombining keyword (lexical) and vector (semantic) search results for better retrieval.Intermediate
• Indirect Prompt InjectionPrompt injection delivered through external content the model processes, such as a web page, email, file or tool output.Intermediate
• Inference ParametersSettings supplied at request time that shape model output, such as temperature, top-p, max tokens and stop sequences.Foundational
• Inference ThroughputThe amount of work an inference system completes per unit time, such as tokens or requests per second.Intermediate
• JailbreakA technique that manipulates a model into bypassing its safety training to produce prohibited content.Intermediate
• JSON SchemaA standard vocabulary for describing the structure and validation rules of JSON data.Intermediate
• Keyword SearchLexical search that matches documents containing the query's exact terms, typically ranked with BM25.Foundational
• Knowledge DistillationTraining a smaller student model to reproduce the outputs or behavior of a larger teacher model.Intermediate
• Knowledge GraphA structured representation of entities and the relationships between them.Intermediate
• Large Language Model (LLM)A very large transformer model trained on massive text data to understand and generate natural language and code.Foundational
• LLM-as-a-JudgeUsing a language model to grade or compare outputs of another model against a rubric.Intermediate
• LLMOpsOperational practices for deploying and managing large language model applications in production.Intermediate
• Lost in the MiddleThe tendency of some LLMs to use information at the start and end of a long context better than information in the middle.Advanced
• Low-Rank Adaptation (LoRA)A PEFT method that freezes base weights and trains small low-rank matrices added to selected layers.Intermediate
• Max TokensAn inference parameter limiting the number of tokens the model may generate in a response.Foundational
• Message Batches APIAn asynchronous API for submitting large volumes of Claude requests at a discount, processed within 24 hours.Intermediate
• Metadata FilteringRestricting vector search to documents whose attributes match conditions such as date, source or access level.Intermediate
• Model EvaluationMeasuring a trained model's performance, robustness and fitness for purpose before and after deployment.Foundational
• Multimodal EmbeddingAn embedding that places different modalities, such as images and text, in the same vector space.Intermediate
• Observability for LLM ApplicationsTracing, logging and measuring prompts, responses, tool calls, tokens, latency and quality in LLM systems.Advanced
• OWASP Top 10 for LLM ApplicationsOWASP's list of the most critical security risks for applications built on large language models.Intermediate
• Parameter-Efficient Fine-Tuning (PEFT)Fine-tuning methods that update only a small set of added or selected parameters while freezing the base model.Intermediate
• Prompt CachingReusing the processed state of a repeated prompt prefix across requests to cut latency and cost.Intermediate
• Prompt ChainingBreaking a complex task into a sequence of prompts where each output feeds the next.Intermediate
• Prompt EngineeringDesigning and refining model inputs to reliably elicit accurate, useful and safe outputs.Foundational
• Prompt InjectionAn attack in which crafted input causes an LLM to ignore its instructions or perform unintended actions.Foundational
• Prompt TemplateA reusable prompt with placeholders that are filled with variables at runtime.Foundational
• Provisioned ThroughputReserved model inference capacity purchased for consistent performance, rather than paying per token on demand.Intermediate
• QuantizationReducing the numeric precision of model weights (and sometimes activations) to shrink memory and speed inference.Intermediate
• Query RewritingReformulating a user's query, often with an LLM, to improve retrieval.Advanced
• RAG EvaluationMeasuring a RAG system's retrieval quality and answer quality, typically with faithfulness, relevance and context metrics.Advanced
• ReActReason + Act: a prompting pattern where a model interleaves reasoning steps with tool actions and observations.Intermediate
• RerankingRe-scoring an initial set of retrieved results with a more accurate model to put the most relevant first.Intermediate
• Retrieval-Augmented Generation (RAG)Retrieving relevant information from external sources at query time and adding it to the prompt so the model answers from it.Foundational
• ROUGERecall-Oriented Understudy for Gisting Evaluation: n-gram and sequence overlap metrics for summarization.Intermediate
• RoutingClassifying an input and directing it to a specialized prompt, tool or model.Intermediate
• Semantic SearchSearch that retrieves results by meaning, using embeddings, rather than by exact keyword match.Foundational
• Sensitive Information DisclosureAn LLM application risk where the model reveals confidential data, PII or secrets in its outputs.Intermediate
• StreamingReturning a model's output incrementally as tokens are generated instead of waiting for the full response.Intermediate
• Structured OutputConstraining model responses to a defined format, typically valid JSON matching a schema.Intermediate
• SummarizationCondensing a document into a shorter version that preserves key information.Foundational
• Supervised Fine-Tuning (SFT)Fine-tuning a model on labeled input-output pairs, such as instruction-response examples.Intermediate
• System PromptInstructions supplied separately from user messages that set a model's role, rules, tone and constraints for a conversation.Foundational
• System Prompt LeakageThe exposure of a system prompt's contents, potentially revealing rules, logic or embedded secrets.Intermediate
• TemperatureAn inference parameter that scales randomness in token selection; low values are more deterministic, high values more creative.Foundational
• Time to First Token (TTFT)The latency between sending a request and receiving the first generated token.Intermediate
• Token PricingCharging for LLM usage per input and output token, often with separate rates for cached and batched tokens.Foundational
• Tool UseA model's ability to request execution of external functions, APIs or programs and use their results.Foundational
• Top-k SamplingSampling the next token only from the k most probable candidates.Intermediate
• Top-p SamplingNucleus sampling: choose the next token from the smallest set whose cumulative probability exceeds p.Intermediate
• Vector DatabaseA database optimized for storing embeddings and performing fast similarity search over them.Foundational
• Vector IndexA data structure that organizes embeddings for efficient similarity search.Intermediate
• vLLMA popular open-source, high-throughput LLM serving engine built around PagedAttention.Advanced
• Workflow OrchestrationCoordinating LLM calls and tools through predefined code paths such as chaining, routing and parallelization.Intermediate