AWS Certified AI Practitioner (AIF-C01) Exam Questions

Domain 1: Fundamentals of AI and ML (20%)

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A retail company wants a system that can learn from historical sales data and improve its demand forecasts automatically over time, without being explicitly reprogrammed for every new pattern in the data.

Which term BEST describes this capability?

⬜ Artificial intelligence (AI)
⬜ Machine learning (ML)
⬜ Business intelligence (BI)
⬜ Robotic process automation (RPA)

Which statement correctly describes the difference between supervised learning and unsupervised learning?

⬜ Supervised learning uses labeled data to learn a mapping from inputs to known outputs, while unsupervised learning finds patterns or structure in unlabeled data.
⬜ Supervised learning only works with image data, while unsupervised learning only works with text data.
⬜ Supervised learning requires no training data, while unsupervised learning requires large labeled datasets.
⬜ Supervised learning and unsupervised learning are two names for the same technique.

A company wants to process a large batch of scanned invoices overnight and does not need results immediately. Which type of inferencing is the MOST cost-effective fit for this use case?

⬜ Real-time inferencing
⬜ Asynchronous inferencing
⬜ Batch inferencing
⬜ Serverless inferencing with sub-second latency requirements

A data science team is building a model to predict which of three shipping carriers will deliver a package fastest, based on structured features like distance, weight, and historical delivery times. Which type of ML technique is this?

⬜ Regression
⬜ Classification
⬜ Clustering
⬜ Reinforcement learning

A company needs to add multi-language customer support to its application by translating incoming support tickets. Which AWS AI service is purpose-built for this use case with the LEAST implementation effort?

⬜ Amazon Comprehend
⬜ Amazon Translate
⬜ Amazon Transcribe
⬜ Amazon Polly

Which scenario is the LEAST appropriate use case for an AI/ML solution?

⬜ Forecasting next month’s product demand based on historical sales trends.
⬜ Detecting potentially fraudulent credit card transactions in near real time.
⬜ Calculating the exact income tax owed by a taxpayer under a fixed, published tax formula.
⬜ Recommending products to a customer based on browsing history.

A company is building a fraud-detection pipeline and wants to identify the AWS services and stages typically involved in an end-to-end AI/ML pipeline. Which sequence BEST reflects the stages of a typical ML pipeline?

⬜ Model deployment, then data collection, then model monitoring, then data labeling.
⬜ Data collection and preparation, model building and training, model evaluation, deployment, and ongoing monitoring/retraining.
⬜ Model training only, since AWS managed services eliminate the need for data preparation or monitoring.
⬜ Data labeling, followed immediately by production deployment, with no evaluation step.

A company has deployed a fraud-detection model and wants to track ongoing model quality metrics like accuracy and precision in production, in addition to business metrics like cost per transaction reviewed. Which concept does this practice belong to?

⬜ Model Context Protocol (MCP)
⬜ MLOps (ML Operations)
⬜ Retrieval Augmented Generation (RAG)
⬜ Prompt engineering

A team evaluating a binary fraud classifier wants a single metric that balances both precision (avoiding false fraud flags) and recall (catching actual fraud). Which metric should they use?

⬜ F1 score
⬜ Mean squared error (MSE)
⬜ R-squared
⬜ BLEU score

Domain 2: Fundamentals of Generative AI (24%)

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A company is deciding between building a custom ML model from scratch versus using a pre-trained foundation model (FM) for a new text-summarization feature. Which factor would MOST justify choosing a pre-trained FM over training a custom model from scratch?

⬜ The company has years of proprietary labeled data and unlimited budget for training infrastructure.
⬜ The company needs a general-purpose capability quickly, without the time and cost of collecting massive training datasets and training a model from zero.
⬜ The company requires a model trained exclusively on its own private data, with no exposure to any external data.
⬜ The company wants the lowest possible per-inference cost regardless of development timeline.

Which term refers to the smallest units of text (such as words or sub-word pieces) that a large language model (LLM) processes and generates?

⬜ Embeddings
⬜ Tokens
⬜ Vectors
⬜ Chunks

A company wants to build a semantic search feature that finds documents with similar meaning to a user’s query, even when they don’t share the same exact keywords. Which underlying GenAI concept makes this possible?

⬜ Prompt templates
⬜ Embeddings
⬜ Diffusion models
⬜ Token-based pricing

Which AWS service allows a company to access a variety of foundation models (FMs) from multiple providers through a single, unified API to build generative AI applications?

⬜ Amazon SageMaker Ground Truth
⬜ Amazon Bedrock
⬜ AWS Glue
⬜ Amazon Kinesis

A generative AI application’s monthly cost varies significantly depending on the number of input and output tokens processed by the foundation model. Which pricing model does this describe?

⬜ Flat monthly subscription
⬜ Token-based pricing
⬜ Per-user licensing
⬜ Free tier only, with no usage-based charges

Which of the following is a well-known DISADVANTAGE of generative AI solutions that a company should account for before deploying a customer-facing chatbot?

⬜ Generative AI models are always fully deterministic and produce identical output every time for the same input.
⬜ Generative AI models can hallucinate, producing plausible-sounding but factually incorrect or fabricated responses.
⬜ Generative AI models cannot generate any conversational responses.
⬜ Generative AI models require no evaluation before production deployment.

A company is comparing several foundation models for a customer-service application and wants to evaluate cross-model performance, return on investment (ROI), and customer satisfaction. Which category of consideration BEST describes this evaluation?

⬜ Determining the business value and metrics of a GenAI application
⬜ Choosing an inference parameter such as temperature
⬜ Selecting a vector database for embeddings
⬜ Defining a prompt template

A company building an autonomous AI agent needs its agent to call external tools, maintain memory across steps, and communicate with other agents in a standardized way. Which foundational agentic AI concept addresses standardizing how an agent connects to external tools and data sources?

⬜ Model Context Protocol (MCP)
⬜ Token-based pricing
⬜ Diffusion models
⬜ BLEU score

Which AWS capability provides curated, pre-built foundation models and solution templates that developers can quickly deploy within Amazon SageMaker AI to accelerate GenAI application development?

⬜ SageMaker JumpStart
⬜ AWS CloudTrail
⬜ Amazon Macie
⬜ AWS Config

A startup wants to launch a generative AI proof-of-concept quickly, with a low upfront cost and without managing GPU infrastructure. Which is an advantage of using AWS managed GenAI services like Amazon Bedrock for this use case?

⬜ It requires the company to build and train a foundation model from scratch.
⬜ It lowers the barrier to entry by providing managed access to foundation models with pay-as-you-go pricing, without needing to manage the underlying infrastructure.
⬜ It eliminates the need for any prompt engineering or evaluation of model output.
⬜ It requires committing to a single foundation model provider with no flexibility to switch models.

A company generating GenAI-produced marketing images at high volume wants to understand the cost tradeoffs of committing to provisioned throughput versus on-demand token-based pricing on Amazon Bedrock. Which factor is the PRIMARY consideration for choosing provisioned throughput?

⬜ Provisioned throughput is always cheaper than on-demand pricing regardless of usage volume.
⬜ Provisioned throughput reserves dedicated model capacity for predictable, high-volume workloads, typically at a different cost structure than on-demand token pricing.
⬜ Provisioned throughput is required before any model can be invoked on Amazon Bedrock.
⬜ Provisioned throughput only applies to model fine-tuning jobs, not inference.

Which statement BEST describes a foundation model (FM)?

⬜ A small, task-specific model trained only on one company’s internal dataset for a single narrow use case.
⬜ A large model pre-trained on a broad range of data that can be adapted to many different downstream tasks through prompting or fine-tuning.
⬜ A rule-based expert system with no learned parameters.
⬜ A model that can only process image data, never text.

A company is evaluating whether generative AI is the right approach for a use case where they need a highly precise, exact numeric output every time (such as a legal filing deadline calculation), rather than a fluent but potentially varying response. Which GenAI limitation is MOST relevant here?

⬜ Adaptability
⬜ Nondeterminism
⬜ Conversational capability
⬜ Content generation ability

A company wants to increase the consistency and quality of large language model (LLM) responses by providing the model with access to external sources of knowledge. Which technique will meet the requirement with the least development effort?

⬜ Pre-train a new foundation model from scratch on the company’s proprietary data.
⬜ Use Retrieval Augmented Generation (RAG) to retrieve relevant external data at query time and include it in the model’s prompt.
⬜ Fine-tune the foundation model on the company’s proprietary data.
⬜ Increase the model’s temperature parameter to make responses more consistent.

Domain 3: Applications of Foundation Models (28%)

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A company wants its customer-support chatbot to answer questions using the most current internal knowledge-base articles, without retraining or fine-tuning the underlying foundation model every time an article changes. Which technique BEST meets this requirement?

⬜ Retrieval Augmented Generation (RAG)
⬜ Full model pre-training from scratch
⬜ Increasing the temperature parameter
⬜ Reducing the maximum output token length

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Which AWS service allows a company to connect Amazon Bedrock foundation models to external data sources such as Amazon S3 for a Retrieval Augmented Generation (RAG) architecture, including automatically converting documents into embeddings?

⬜ Knowledge Bases for Amazon Bedrock
⬜ AWS Direct Connect
⬜ Amazon CloudFront
⬜ AWS Snowball

A company implementing a RAG solution on AWS needs to store vector embeddings so they can be efficiently searched for semantic similarity. Which of the following is a valid AWS option for storing and querying vector embeddings?

⬜ Amazon OpenSearch Service
⬜ Amazon Simple Notification Service (SNS)
⬜ AWS Step Functions
⬜ Amazon Simple Queue Service (SQS)

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A developer wants a foundation model’s responses to be MORE deterministic and focused, consistently choosing the most likely next token rather than exploring less probable alternatives. Which inference parameter adjustment achieves this?

⬜ Increase the temperature value.
⬜ Decrease the temperature value.
⬜ Increase the maximum output token length.
⬜ Enable a higher top-p value only.

A prompt engineer provides a large language model with several example input-output pairs directly within the prompt before asking it to complete a new, similar task. Which prompt engineering technique is being used?

⬜ Zero-shot prompting
⬜ Few-shot prompting
⬜ Retrieval Augmented Generation (RAG)
⬜ Fine-tuning

Which of the following is a well-documented RISK of prompt engineering that a company should guard against when exposing a chatbot to public users?

⬜ Prompt hijacking, where a malicious user manipulates the model into ignoring its original instructions.
⬜ Improved response quality from well-structured prompts.
⬜ Faster experimentation and iteration on prompt design.
⬜ Clearer, more specific outputs due to well-crafted instructions.

A company wants to save, version, and reuse a library of tested prompts across multiple generative AI applications and team members on Amazon Bedrock. Which capability should they use?

⬜ Amazon Bedrock Prompt Management
⬜ Amazon Bedrock Guardrails
⬜ AWS Config
⬜ Amazon Inspector

A company wants to adapt a general-purpose foundation model to understand company-specific terminology and respond in a particular tone, by further training the model on a smaller, labeled, domain-specific dataset. Which FM customization approach is this?

⬜ Fine-tuning
⬜ Retrieval Augmented Generation (RAG)
⬜ Prompt engineering with zero-shot prompts
⬜ Increasing the temperature parameter only

Which customization approach generally has the LOWEST upfront cost and fastest time-to-value, since it requires no additional model training?

⬜ Pre-training a foundation model from scratch
⬜ Fine-tuning with a large labeled dataset
⬜ In-context learning through prompt engineering (zero-shot/few-shot)
⬜ Continuous pre-training on a domain corpus

A company wants to improve a foundation model’s responses using human feedback about which of several generated responses is preferred, and use that feedback signal to further align the model’s behavior. Which technique does this describe?

⬜ Reinforcement learning from human feedback (RLHF)
⬜ Zero-shot prompting
⬜ Vector embedding generation
⬜ Model distillation

A company wants a smaller, faster, and cheaper model that mimics the behavior of a larger, more capable foundation model for a narrower production use case. Which technique BEST achieves this?

⬜ Model distillation
⬜ Retrieval Augmented Generation (RAG)
⬜ Prompt hijacking
⬜ Increasing the context window size

A team wants to evaluate the quality of a foundation model’s generated text summaries against human-written reference summaries using an automated, well-established metric. Which metric is commonly used for this purpose?

⬜ ROUGE
⬜ F1 score
⬜ Precision and recall only
⬜ Mean squared error (MSE)

A company wants to run a structured evaluation job on Amazon Bedrock that compares multiple foundation models against built-in metrics and, optionally, human review. Which Amazon Bedrock capability supports this?

⬜ Amazon Bedrock Model Evaluation
⬜ Amazon Bedrock Guardrails
⬜ AWS Trusted Advisor
⬜ Amazon Macie

A company deployed a RAG-based question-answering agent and wants to measure whether end users actually complete their intended task and are satisfied with the answers, beyond just automated text-quality metrics. Which type of metric BEST addresses this?

⬜ Business objective alignment metrics, such as task completion rate and user satisfaction.
⬜ BLEU score only
⬜ ROUGE score only
⬜ Token count per request

A software development company is working on a new product with a very tight deadline. The company needs a way to develop code faster without sacrificing reliability or security. Which service could best help this company meet its deadline?

⬜ Amazon SageMaker JumpStart
⬜ Amazon Bedrock
⬜ Amazon Q Business
⬜ Amazon Q Developer

Domain 4: Guidelines for Responsible AI (14%)

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A company wants to detect and filter harmful, biased, or off-topic content generated by its foundation model before it reaches end users. Which Amazon Bedrock capability is purpose-built for this?

⬜ Amazon Bedrock Guardrails
⬜ Amazon Bedrock Prompt Management
⬜ Amazon SageMaker JumpStart
⬜ AWS Config

A company training a hiring-recommendation model discovers that its training dataset contains far more examples from one demographic group than others. What is the PRIMARY risk this creates?

⬜ The model will train faster because the dataset is smaller.
⬜ The model may produce biased predictions that unfairly disadvantage underrepresented groups.
⬜ The model will automatically balance the dataset during training with no risk.
⬜ The model will be immune to overfitting.

Which AWS capability helps document a machine learning model’s intended use, training details, risk ratings, and evaluation results in a single, structured, shareable record to support transparency and governance?

⬜ Amazon SageMaker Model Cards
⬜ AWS Direct Connect
⬜ Amazon Kinesis Data Streams
⬜ AWS Snowmobile

A company is evaluating the legal risks of deploying a generative AI application that produces marketing copy and images. Which of the following is a recognized legal risk category associated with generative AI?

⬜ Guaranteed accuracy of all generated content
⬜ Intellectual property (IP) infringement claims from content that resembles copyrighted material
⬜ Elimination of all end-user risk
⬜ Complete removal of the need for human review

Which AWS capability can help a data science team analyze training data and model predictions for potential bias across different demographic groups, both before and after training?

⬜ Amazon SageMaker Clarify
⬜ AWS Snowball Edge
⬜ Amazon CloudFront
⬜ AWS Direct Connect

A company wants end users to understand, in general terms, why an AI system produced a particular recommendation, and wants to incorporate a feedback mechanism so users can flag incorrect or concerning outputs. Which responsible-AI principle does this BEST reflect?

⬜ Human-centered design for explainable AI
⬜ Maximizing model throughput
⬜ Minimizing infrastructure cost
⬜ Reducing the number of foundation model parameters

A company must choose between a highly transparent, simpler model and a more complex, higher-performing model that is harder to interpret. Which concept describes this decision?

⬜ The tradeoff between model interpretability and performance.
⬜ The tradeoff between AWS Regions and Availability Zones.
⬜ The tradeoff between synchronous and asynchronous networking.
⬜ The tradeoff between block storage and object storage.

A deep learning system developer doesn’t document the origin of the training data or whether it has been manipulated. Reviewers raise concerns about fairness and bias in the system’s results. Which step of promoting AI training transparency does this scenario represent?

⬜ Model evaluation
⬜ Human-in-the-loop review
⬜ Data lineage and provenance documentation
⬜ Model monitoring

Domain 5: Security, Compliance, and Governance for AI Solutions (14%)

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Under the AWS Shared Responsibility Model, who is responsible for securing an AI/ML workload’s data and configuring appropriate access controls (such as IAM policies) for resources like Amazon Bedrock or Amazon SageMaker AI?

⬜ AWS is solely responsible for all security, with no customer responsibility.
⬜ The customer is responsible for security ‘in’ the cloud, including data protection and access management, while AWS is responsible for security ‘of’ the cloud infrastructure.
⬜ Security responsibility is randomly assigned per project.
⬜ Only AWS Support is responsible for customer data security.

A company wants to grant fine-grained permissions so that only specific users can invoke certain Amazon Bedrock foundation models, while denying access to others. Which AWS service should be used to define and enforce these permissions?

⬜ AWS Identity and Access Management (IAM)
⬜ Amazon Macie
⬜ Amazon Inspector
⬜ AWS Artifact

A company storing training data in Amazon S3 wants to automatically discover and alert on personally identifiable information (PII) present in its data before it is used to train or fine-tune a model. Which AWS service is purpose-built for this?

⬜ Amazon Macie
⬜ AWS Config
⬜ Amazon Inspector
⬜ AWS Trusted Advisor

A company wants to invoke Amazon Bedrock APIs from within its Amazon VPC without traversing the public internet, to reduce exposure of sensitive AI workload traffic. Which AWS capability should be used?

⬜ AWS PrivateLink
⬜ Amazon Route 53 public hosted zones
⬜ An internet gateway
⬜ AWS Direct Connect only, with a NAT gateway

A company must track which datasets and configurations were used to produce a particular version of a deployed model, to satisfy an audit request. Which SageMaker capability BEST supports this requirement?

⬜ SageMaker Lineage Tracking
⬜ SageMaker JumpStart
⬜ SageMaker Ground Truth
⬜ SageMaker Studio Notebooks only

A company needs to continuously evaluate whether its AWS resources supporting an AI workload comply with internal security and configuration policies, and be alerted when a resource drifts out of compliance. Which AWS service is designed for this?

⬜ AWS Config
⬜ Amazon Polly
⬜ Amazon Lex
⬜ Amazon Comprehend

A company wants a complete, immutable audit trail of every API call made against its Amazon Bedrock and Amazon SageMaker AI resources, including who made the call and when, to support a compliance review. Which AWS service provides this?

⬜ AWS CloudTrail
⬜ AWS Trusted Advisor
⬜ AWS Artifact
⬜ Amazon Inspector