Free Practice Questions for AWS Certified AI Practitioner (AIF-C01) Certification

Objectives:

• Define basic AI terms (for example, AI, ML, deep learning, neural networks, computer vision, natural language processing [NLP], model, algorithm, training and inferencing, bias, fairness, fit, large language models(LLMs)). • Describe the similarities and differences between AI, ML, GenAI, and deep learning. • Describe various types of inferencing (for example, batch, real-time). • Describe the different types of data in AI models (for example, labeled and unlabeled, tabular, time-series, image, text, structured and unstructured). • Describe supervised learning, unsupervised learning, and reinforcement learning.

Objectives:

• Recognize applications where AI/ML can provide value (for example, assist human decision making, solution scalability, automation). • Determine when AI/ML solutions are not appropriate (for example, cost-benefit analyses, situations when a specific outcome is needed instead of a prediction). • Select the appropriate ML techniques for specific use cases (for example, regression, classification, clustering). • Identify examples of real-world AI applications (for example, computer vision, NLP, speech recognition, recommendation systems, fraud detection, forecasting). • Explain the capabilities of AWS managed AI/ML services (for example, Amazon SageMaker AI, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, Amazon Polly).

Objectives:

• Describe components of an ML pipeline (for example, data collection, exploratory data analysis [EDA], data pre-processing, feature engineering, model training, hyperparameter tuning, evaluation, deployment, monitoring). • Describe sources of ML models (for example, open source pre-trained models, training custom models). • Describe methods to use a model in production (for example, managed API service, self-hosted API). • Identify relevant AWS services and features for each stage of an ML pipeline (for example, SageMaker AI, SageMaker Data Wrangler, SageMaker Feature Store, SageMaker Model Monitor). • Describe fundamental concepts of ML operations (MLOps) (for example, experimentation, repeatable processes, scalable systems, managing technical debt, achieving production readiness, model monitoring, model re-training). • Describe model performance metrics (for example, accuracy, Area Under the Curve [AUC], F1 score) and business metrics (for example, cost per user, development costs, customer feedback, return on investment [ROI]) to evaluate ML models.

Objectives:

• Define foundational GenAI concepts (for example, tokens, chunking, embeddings, vectors, prompt engineering, transformer-based LLMs, foundation models [FMs], multimodal models, diffusion models). • Identify potential use cases for GenAI models (for example, image, video, and audio generation; summarization; AI assistants; translation; code generation; customer service agents; search; recommendation engines). • Describe the foundation model lifecycle (for example, data selection, model selection, pre-training, fine-tuning, evaluation, deployment, feedback).

Objectives:

• Describe the advantages of GenAI (for example, adaptability, responsiveness, simplicity). • Identify disadvantages of GenAI solutions (for example, hallucinations, interpretability, inaccuracy, nondeterminism). • Identify factors to consider when selecting GenAI models (for example, model types, performance requirements, capabilities, constraints, compliance). • Determine business value and metrics for GenAI applications (for example, cross-domain performance, efficiency, conversion rate, average revenue per user, accuracy, customer lifetime value).

Objectives:

• Identify AWS services and features to develop GenAI applications (for example, Amazon SageMaker JumpStart, Amazon Bedrock PartyRock, Amazon Q, Amazon Bedrock Data Automation). • Describe the advantages of using AWS GenAI services to build applications (for example, accessibility, lower barrier to entry, efficiency, cost-effectiveness, speed to market, ability to meet business objectives). • Describe the benefits of AWS infrastructure for GenAI applications (for example, security, compliance, responsibility, safety). • Describe cost tradeoffs of AWS GenAI services (for example, responsiveness, availability, redundancy, performance, regional coverage, token-based pricing, provision throughput, custom models).

Objectives:

• Identify selection criteria to choose pre-trained models (for example, cost, modality, latency, multi-lingual, model size, model complexity, customization, input/output length, prompt caching). • Describe the effect of inference parameters on model responses (for example, temperature, input/output length). • Define Retrieval Augmented Generation (RAG) and describe its business applications (for example, Amazon Bedrock Knowledge Bases). • Identify AWS services that help store embeddings within vector databases (for example, Amazon OpenSearch Service, Amazon Aurora, Amazon Neptune, Amazon RDS for PostgreSQL). • Explain the cost tradeoffs of various approaches to FM customization (for example, pre-training, fine-tuning, in-context learning, RAG). • Describe the role of agents in multi-step tasks (for example, Amazon Bedrock Agents, agentic AI, model context protocol).

Objectives:

• Define the concepts and constructs of prompt engineering (for example, context, instruction, negative prompts, model latent space, prompt routing). • Define techniques for prompt engineering (for example, chain-of-thought, zero-shot, single-shot, few-shot, prompt templates). • Identify and describe the benefits and best practices for prompt engineering (for example, response quality improvement, experimentation, guardrails, discovery, specificity and concision, using multiple comments). • Define potential risks and limitations of prompt engineering (for example, exposure, poisoning, hijacking, jailbreaking).

Objectives:

• Describe the key elements of training an FM (for example, pre-training, fine-tuning, continuous pre-training, distillation). • Define methods for fine-tuning an FM (for example, instruction tuning, adapting models for specific domains, transfer learning, continuous pre-training). • Describe how to prepare data to fine-tune an FM (for example, data curation, governance, size, labeling, representativeness, reinforcement learning from human feedback [RLHF]).

Objectives:

• Determine approaches to evaluate FM performance (for example, human evaluation, benchmark datasets, Amazon Bedrock Model Evaluation). • Identify relevant metrics to assess FM performance (for example, Recall-Oriented Understudy for Gisting Evaluation [ROUGE], Bilingual Evaluation Understudy [BLEU], BERTScore). • Determine whether a FM effectively meets business objectives (for example, productivity, user engagement, task engineering). • Identify approaches to evaluate the performance of applications built with FMs (for example, RAG, agents, workflows).

Objectives:

• Identify features of responsible AI (for example, bias, fairness, inclusivity, robustness, safety, veracity). • Explain how to use tools to identify features of responsible AI (for example, Amazon Bedrock Guardrails). • Define responsible practices to select a model (for example, environmental considerations, sustainability). • Identify legal risks of working with GenAI (for example, intellectual property infringement claims, biased model outputs, loss of customer trust, end user risk, hallucinations). • Identify characteristics of datasets (for example, inclusivity, diversity, curated data sources, balanced datasets). • Describe effects of bias and variance (for example, effects on demographic groups, inaccuracy, overfitting, underfitting). • Describe tools to detect and monitor bias, trustworthiness, and truthfulness (for example, analyzing label quality, human audits, subgroup analysis, Amazon SageMaker Clarify, SageMaker Model Monitor, Amazon Augmented AI [Amazon A2I]).

Objectives:

• Describe the differences between models that are transparent and explainable and models that are not transparent and explainable. • Describe tools to identify transparent and explainable models (for example, SageMaker Model Cards, open source models, data, licensing). • Identify tradeoffs between model safety and transparency (for example, measure interpretability and performance). • Describe principles of human-centered design for explainable AI.

Objectives:

• Identify AWS services and features to secure AI systems (for example, IAM roles, policies, and permissions; encryption; Amazon Macie; AWS PrivateLink; AWS shared responsibility model). • Describe the concept of source citation and documenting data origins (for example, data lineage, data cataloging, Amazon SageMaker Model Cards). • Describe best practices for secure data engineering (for example, assessing data quality, implementing privacy-enhancing technologies, data access control, data integrity). • Describe security and privacy considerations for AI systems (for example, application security, threat detection, vulnerability management, infrastructure protection, prompt injection, encryption at rest and in transit).

Objectives:

• Identify AWS services and features to assist with governance and regulation compliance (for example, AWS Config, Amazon Inspector, AWS Audit Manager, AWS Artifact, AWS CloudTrail, AWS Trusted Advisor). • Describe data governance strategies (for example, data lifecycles, logging, residency, monitoring, observation, retention). • Describe processes to follow governance protocols (for example, policies, review cadence, review strategies, governance frameworks such as the Generative AI Security Scoping Matrix, transparency standards, team training requirements).