AWS Certified AI Practitioner: key terms

Flashcards for this exam AWS Certified AI Practitioner guide Class dates

• AccuracyThe fraction of all predictions that are correct: (TP + TN) / total.Foundational
• AI AgentAn AI system that uses a model to pursue a goal autonomously by planning, calling tools and acting on results in a loop.Foundational
• Algorithmic BiasSystematic, unfair errors in AI outputs that disadvantage certain groups, often inherited from data.Foundational
• 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 CanvasA no-code SageMaker AI interface for preparing data, building ML models and using generative AI.Intermediate
• Amazon SageMaker ClarifyA SageMaker AI capability for detecting bias in data and models and explaining predictions.Intermediate
• Amazon SageMaker Ground TruthAWS service for creating labeled training datasets using human workforces and automated labeling.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
• Anomaly DetectionIdentifying data points, events or patterns that deviate significantly from expected behavior.Intermediate
• Area Under the Curve (AUC)The area under the ROC curve, summarizing a classifier's ability to rank positives above negatives (0.5 = random, 1.0 = perfect).Intermediate
• Artificial Intelligence (AI)The field of building computer systems that perform tasks normally requiring human intelligence, such as reasoning, perception and language.Foundational
• AutoMLAutomated machine learning: tools that automate algorithm selection, feature preprocessing and hyperparameter tuning.Foundational
• AWS AI ServicesPretrained, task-specific AWS APIs such as Rekognition, Comprehend, Textract, Transcribe, Polly, Translate, Lex, Kendra and Personalize.Foundational
• BackpropagationThe algorithm that computes gradients of the loss with respect to every weight by applying the chain rule backward through the network.Intermediate
• Batch InferenceGenerating predictions for a large dataset offline in a single job rather than per request.Foundational
• BenchmarkA standardized dataset and metric used to compare model capabilities.Intermediate
• BERTBidirectional Encoder Representations from Transformers: Google's 2018 encoder-only model pretrained with masked language modeling.Intermediate
• BERTScoreA text-similarity metric that compares contextual embeddings of generated and reference text.Advanced
• Bias-Variance TradeoffThe balance between error from overly simple assumptions (bias) and error from sensitivity to training data (variance).Intermediate
• Binary ClassificationClassification with exactly two possible output classes, such as yes/no or positive/negative.Foundational
• BLEUBilingual Evaluation Understudy: a metric scoring generated text by n-gram precision overlap with reference text.Intermediate
• Chain-of-Thought Prompting (CoT)Prompting a model to reason step by step before giving a final answer.Intermediate
• ChunkingSplitting documents into smaller passages before embedding them for retrieval.Intermediate
• Class ImbalanceA dataset where one class is far more frequent than others, which can bias models toward the majority class.Intermediate
• ClassificationA supervised learning task that predicts which discrete category or class an input belongs to.Foundational
• Classification ThresholdThe probability cutoff above which a classifier predicts the positive class.Intermediate
• ClaudeAnthropic's family of large language models, available in Opus, Sonnet and Haiku tiers.Foundational
• ClusteringAn unsupervised task that groups similar data points together so items in a cluster are more alike than items in other clusters.Foundational
• Collaborative FilteringRecommending items based on the preferences of similar users or similarity between items in interaction data.Intermediate
• Concept DriftA change in the relationship between inputs and the target variable over time.Intermediate
• Confusion MatrixA table comparing predicted classes with actual classes, showing true/false positives and negatives.Foundational
• Content FilteringAutomatically detecting and blocking harmful categories of content in AI inputs and outputs.Foundational
• Context WindowThe maximum number of tokens (input plus output) a model can consider in a single request.Foundational
• Continued PretrainingFurther self-supervised training of a foundation model on large unlabeled domain data.Advanced
• Convolutional Neural Network (CNN)A neural network that uses convolutional filters to detect spatial patterns, primarily for images.Intermediate
• Cosine SimilarityA similarity measure based on the angle between two vectors, ranging from -1 to 1.Intermediate
• Cross-ValidationA resampling method that trains and evaluates a model on multiple different splits to estimate generalization more reliably.Intermediate
• Data DriftA change in the statistical distribution of input data in production compared with training data.Intermediate
• Data LabelingThe process of annotating raw data with labels so it can be used for supervised training or evaluation.Foundational
• Decision TreeA model that predicts by learning a hierarchy of if-then splits on feature values.Foundational
• Decoding StrategyThe method used to choose each next token from the model's probability distribution.Intermediate
• Deep Learning (DL)A subset of machine learning that uses multi-layer neural networks to learn hierarchical representations directly from raw data.Foundational
• Diffusion ModelA generative model that learns to create data by reversing a gradual noising process.Intermediate
• Dimensionality ReductionTechniques that reduce the number of input features while preserving as much useful information as possible.Intermediate
• Discriminative ModelA model that learns the boundary between classes, estimating P(label | input).Intermediate
• Edge AIRunning AI inference on devices near the data source, such as cameras, robots or phones, rather than in the cloud.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
• Encoder-Only ArchitectureA transformer that uses only the encoder stack with bidirectional attention, suited to understanding tasks.Intermediate
• EpochOne complete pass of the training algorithm through the entire training dataset.Foundational
• ExplainabilityThe ability to describe why an AI model produced a particular output in terms humans can understand.Foundational
• F1 ScoreThe harmonic mean of precision and recall: 2PR / (P + R).Foundational
• FairnessThe principle that AI systems should not produce unjustly different outcomes for individuals or groups.Foundational
• FeatureAn individual measurable input variable that a model uses to make predictions.Foundational
• Feature EngineeringCreating, transforming and selecting input variables to improve model performance.Foundational
• Few-Shot PromptingIncluding several examples of inputs and desired outputs in the prompt to guide the model.Foundational
• 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
• Generative Adversarial Network (GAN)A generative model in which a generator and a discriminator are trained against each other.Intermediate
• Generative AI (GenAI)AI that creates new content such as text, images, code, audio or video based on patterns learned from training data.Foundational
• Gradient BoostingAn ensemble technique that builds models sequentially, each one correcting the residual errors of the previous ones.Intermediate
• Ground TruthThe verified correct answer or label used to train and evaluate models.Foundational
• 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
• Hugging FaceA platform and open-source community hosting models, datasets and libraries such as Transformers.Foundational
• Human EvaluationAssessment of model outputs by human raters, often treated as the gold standard for quality.Foundational
• Human OversightEnsuring humans can monitor, understand, intervene in and override AI system behavior.Foundational
• Human-in-the-Loop (HITL)Design in which humans review, approve or correct AI outputs or actions at defined points.Foundational
• HyperparameterA configuration value set before training that controls the learning process, rather than being learned from data.Foundational
• Hyperparameter TuningSearching for the hyperparameter values that give the best validation performance.Intermediate
• Image ClassificationA computer vision task that assigns a label to an entire image.Foundational
• Image SegmentationA computer vision task that labels each pixel of an image with a class or object instance.Intermediate
• ImputationFilling in missing values in a dataset with estimated substitutes.Foundational
• In-Context Learning (ICL)A model's ability to learn a task from instructions and examples in the prompt without weight updates.Intermediate
• InferenceUsing a trained model to make predictions or generate outputs on new data.Foundational
• Inference ParametersSettings supplied at request time that shape model output, such as temperature, top-p, max tokens and stop sequences.Foundational
• Instruction TuningSupervised fine-tuning on many instruction-response examples so a model follows natural-language instructions.Intermediate
• K-Means ClusteringAn unsupervised algorithm that partitions data into k clusters by assigning points to the nearest centroid and iteratively updating centroids.Foundational
• K-Nearest Neighbors (k-NN)A non-parametric algorithm that predicts by majority vote or average of the k closest training examples.Intermediate
• Knowledge CutoffThe date after which a model has no information from its training data.Foundational
• LabelThe known target value attached to a training example that a supervised model learns to predict.Foundational
• Large Language Model (LLM)A very large transformer model trained on massive text data to understand and generate natural language and code.Foundational
• Linear RegressionA regression algorithm that models the target as a weighted sum of input features plus an intercept.Foundational
• Logistic RegressionA classification algorithm that applies the sigmoid function to a linear combination of features to output a probability.Foundational
• Machine Learning (ML)A subset of AI in which systems learn patterns from data to make predictions or decisions without being explicitly programmed with rules.Foundational
• Machine Learning Operations (MLOps)Practices and tooling that apply DevOps principles to build, deploy, monitor and maintain ML models reliably in production.Foundational
• Machine Translation (MT)Automatically translating text or speech from one language to another.Foundational
• Max TokensAn inference parameter limiting the number of tokens the model may generate in a response.Foundational
• Mean Absolute Error (MAE)The average of absolute differences between predicted and actual values.Foundational
• Mean Squared Error (MSE)The average of squared differences between predicted and actual values.Foundational
• Model CardA document describing a model's intended use, performance, limitations, training data and ethical considerations.Foundational
• Model EvaluationMeasuring a trained model's performance, robustness and fitness for purpose before and after deployment.Foundational
• Model MonitoringContinuously tracking a deployed model's inputs, outputs, performance and fairness to detect degradation.Intermediate
• Model ParametersThe internal values, such as weights and biases, that a model learns from training data.Foundational
• Model ServingHosting a trained model behind an interface so applications can request predictions.Foundational
• Model TrainingThe process of fitting a model's parameters to data by optimizing an objective function.Foundational
• Multimodal ModelA model that can process or generate more than one data type, such as text, images, audio and video.Foundational
• Named Entity Recognition (NER)An NLP task that identifies and classifies entities such as people, organizations, locations and dates in text.Foundational
• Natural Language Processing (NLP)The field of AI that enables computers to understand, interpret and generate human language.Foundational
• Neural Network (NN)A model made of layers of interconnected artificial neurons that learn weighted transformations of input data.Foundational
• Object DetectionA computer vision task that identifies and locates multiple objects in an image with bounding boxes.Intermediate
• One-Hot EncodingRepresenting a categorical variable as multiple binary columns, one per category.Foundational
• Open-Weight ModelA model whose trained weights are publicly downloadable for self-hosting and customization.Intermediate
• Optical Character Recognition (OCR)Technology that extracts machine-readable text from images or scanned documents.Foundational
• OverfittingWhen a model learns training data too closely, including noise, and performs poorly on new data.Foundational
• PerplexityA language-model metric measuring how well a model predicts text; lower perplexity means better prediction.Intermediate
• PrecisionOf all positive predictions, the fraction that are actually positive: TP / (TP + FP).Foundational
• PretrainingThe initial, compute-intensive training of a model on vast unlabeled data to learn general representations.Intermediate
• PromptThe input text (and optionally images or files) given to a generative model to direct its output.Foundational
• Prompt EngineeringDesigning and refining model inputs to reliably elicit accurate, useful and safe outputs.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
• R-Squared (R²)The coefficient of determination: the proportion of variance in the target explained by the model.Intermediate
• Random ForestAn ensemble of decision trees trained on random data samples and feature subsets, averaging their predictions.Intermediate
• Real-Time InferenceServing predictions synchronously with low latency in response to individual requests.Foundational
• RecallOf all actual positives, the fraction the model correctly identifies: TP / (TP + FN). Also called sensitivity or true positive rate.Foundational
• Recommendation SystemA system that predicts items a user is likely to want, based on behavior, item attributes or both.Intermediate
• Recurrent Neural Network (RNN)A neural network that processes sequences by passing a hidden state from one step to the next.Intermediate
• RegressionA supervised learning task that predicts a continuous numeric value.Foundational
• RegularizationTechniques that constrain model complexity to reduce overfitting.Intermediate
• Reinforcement Learning (RL)Learning in which an agent takes actions in an environment and improves its policy by maximizing cumulative reward.Foundational
• Reinforcement Learning from Human Feedback (RLHF)Aligning a model by training a reward model on human preference rankings and optimizing the model against it.Intermediate
• Responsible AIDeveloping and using AI in ways that are fair, reliable, safe, private, secure, transparent, accountable and inclusive.Foundational
• 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
• ROC CurveReceiver Operating Characteristic curve: a plot of true positive rate against false positive rate across thresholds.Intermediate
• Role PromptingAssigning the model a persona or expertise in the prompt to shape its tone and focus.Foundational
• Root Mean Squared Error (RMSE)The square root of MSE, expressing typical prediction error in the target's own units.Foundational
• ROUGERecall-Oriented Understudy for Gisting Evaluation: n-gram and sequence overlap metrics for summarization.Intermediate
• Self-Supervised Learning (SSL)Learning in which the training signal is generated automatically from the data itself, without human labels.Intermediate
• Semantic SearchSearch that retrieves results by meaning, using embeddings, rather than by exact keyword match.Foundational
• Semi-Supervised LearningLearning that combines a small labeled dataset with a large unlabeled dataset to improve model accuracy.Intermediate
• Sentiment AnalysisAn NLP task that determines whether text expresses positive, negative or neutral opinion.Foundational
• SHAPSHapley Additive exPlanations: a method that attributes a prediction to each feature using game-theoretic Shapley values.Intermediate
• Small Language Model (SLM)A language model with relatively few parameters (typically under ~15B) designed for efficiency, edge or cost-sensitive use.Intermediate
• Speech-to-Text (STT)Converting spoken audio into written text; also called automatic speech recognition (ASR).Foundational
• Stable DiffusionA family of open-weight latent diffusion models from Stability AI for text-to-image generation.Intermediate
• SummarizationCondensing a document into a shorter version that preserves key information.Foundational
• Supervised LearningMachine learning that trains a model on labeled examples, where each input is paired with the correct output.Foundational
• Synthetic DataArtificially generated data used to train, fine-tune or evaluate models.Intermediate
• System PromptInstructions supplied separately from user messages that set a model's role, rules, tone and constraints for a conversation.Foundational
• TemperatureAn inference parameter that scales randomness in token selection; low values are more deterministic, high values more creative.Foundational
• Test DatasetData kept completely separate until the end to measure a final model's generalization performance.Foundational
• Text-to-ImageGenerating images from natural-language descriptions.Foundational
• Text-to-Speech (TTS)Generating natural-sounding spoken audio from text.Foundational
• Time Series ForecastingPredicting future values of a variable from its historical, time-ordered observations.Intermediate
• TokenThe basic unit of text an LLM processes, typically a word, subword, character or punctuation mark.Foundational
• Token PricingCharging for LLM usage per input and output token, often with separate rates for cached and batched tokens.Foundational
• Tokenization (NLP)Splitting text into tokens that a model can convert to numeric IDs.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
• Train-Test SplitDividing a dataset into separate subsets for training and evaluation.Foundational
• Training DatasetThe portion of data used to fit a model's parameters.Foundational
• Transfer LearningReusing a model trained on one task as the starting point for a related task.Intermediate
• TransformerA neural network architecture based on self-attention that processes all tokens in a sequence in parallel; the basis of modern LLMs.Foundational
• TransparencyOpenness about how an AI system works, its data, limitations and when people are interacting with AI.Foundational
• UnderfittingWhen a model is too simple to capture underlying patterns and performs poorly on both training and new data.Foundational
• Unsupervised LearningMachine learning that finds structure, groupings or patterns in data that has no labels.Foundational
• Validation DatasetHeld-out data used during development to tune hyperparameters and select models.Foundational
• Vector DatabaseA database optimized for storing embeddings and performing fast similarity search over them.Foundational
• WatermarkingEmbedding detectable signals in AI-generated content to identify it as machine-made.Intermediate
• Zero-Shot PromptingAsking a model to perform a task with instructions only and no examples.Foundational