AWS AIF-C01 Exam Flashcards

Q: How many questions are on the AWS AI Practitioner exam?

The AWS Certified AI Practitioner AIF-C01 exam has 65 total questions: 50 scored questions and 15 unscored questions. You have 90 minutes, and AWS reports results on a 100-1000 scale with 700 as the passing score.

Q: What should I study first for AWS AI Practitioner flashcards?

Start with AI and machine learning foundations, then move into generative AI, foundation models, Amazon Bedrock use cases, responsible AI, security, and governance. The flashcards are built for recognition and scenario vocabulary rather than hands-on model building.

Q: How long should I study for AWS AI Practitioner?

Most candidates should plan on 3-6 weeks depending on AWS and AI/ML familiarity. People new to cloud AI should spend extra time on Bedrock, RAG, model evaluation, data privacy, and responsible AI tradeoffs.

Question 1

AIF-C01 exam identity

Accepted Answer

AIF-C01 is the exam code for AWS Certified AI Practitioner, a Foundational AWS Certification for people who understand AI/ML solutions on AWS without necessarily building them.

Question 2

AIF-C01 format

Accepted Answer

The exam has 65 total questions and a 90-minute time limit. AWS states that 50 questions are scored and 15 are unscored, but unscored questions are not identified during the exam.

Question 3

AIF-C01 score and pass-rate facts

Accepted Answer

AWS reports scores on a 100-1000 scale, with 700 as the minimum passing score. AWS does not publish an official pass-rate percentage for this exam in the local official-source notes.

Question 4

AI vs machine learning

Accepted Answer

Artificial intelligence is the broader capability of making systems perform tasks that seem intelligent. Machine learning is one way to do that by learning patterns from data.

Question 5

Deep learning

Accepted Answer

Deep learning uses layered neural networks to learn complex patterns. It commonly supports computer vision, speech recognition, NLP, and many foundation model behaviors.

Question 6

Training vs inference

Accepted Answer

Training creates or adapts a model from data. Inference uses an already trained model to produce a prediction, classification, score, recommendation, text, or image.

Question 7

Supervised learning

Accepted Answer

Supervised learning uses labeled examples where the desired answer is known. It is common for classification, such as spam detection, and regression, such as price prediction.

Question 8

Unsupervised learning

Accepted Answer

Unsupervised learning looks for patterns in unlabeled data. Typical uses include clustering customers into segments or finding unusual records for further review.

Question 9

Reinforcement learning

Accepted Answer

Reinforcement learning trains an agent through rewards and penalties. For AIF-C01, know the concept, but most practitioner decisions focus on more common business AI patterns.

Question 10

When not to use AI

Accepted Answer

A no-AI approach can be best when rules are deterministic, data is poor, value is weak, risk is unmanaged, or a simple workflow already solves the problem reliably.

Question 11

Classification

Accepted Answer

Classification predicts a category, such as approved or denied, fraudulent or legitimate, or urgent or non-urgent. The output is a class label.

Question 12

Regression

Accepted Answer

Regression predicts a continuous number, such as demand, price, risk score, wait time, or quantity. The output is numeric rather than a category.

Question 13

Forecasting

Accepted Answer

Forecasting predicts future values from time-series patterns. It is useful for demand, inventory, staffing, or capacity planning when historical time-based data is meaningful.

Question 14

Recommendation

Accepted Answer

Recommendation systems rank items or actions for a user, such as products, content, or next-best offers. Good recommendations depend on relevant interaction and item data.

Question 15

Model drift

Accepted Answer

Model drift happens when live data, behavior, labels, or business conditions change enough that model performance degrades after deployment. Monitoring and retraining plans address it.

Question 16

Overfitting

Accepted Answer

Overfitting means a model learned the training data too closely and fails to generalize to new data. Strong training results with weak test results are a common clue.

Question 17

Accuracy risk

Accepted Answer

Accuracy can mislead when classes are imbalanced or errors have unequal cost. A fraud model can look accurate by mostly predicting no fraud while missing important cases.

Question 18

Precision

Accepted Answer

Precision asks: of the items the model marked positive, how many were actually positive? It matters when false positives are costly or disruptive.

Question 19

Recall

Accepted Answer

Recall asks: of all actual positive cases, how many did the model catch? It matters when missing a positive case is risky, such as fraud or safety detection.

Question 20

Foundation model

Accepted Answer

A foundation model is a broad pretrained model that can be adapted to many tasks through prompting, retrieval, or customization instead of training from scratch for every task.

Question 21

Large language model (LLM)

Accepted Answer

An LLM is a foundation model focused on language tasks such as drafting, summarizing, answering, transforming, classifying, and reasoning over text.

Question 22

Tokens

Accepted Answer

Tokens are the units a generative model reads and writes. Token count affects cost, latency, context size, and how much output the model can produce.

Question 23

Context window

Accepted Answer

The context window is the space available for instructions, user input, retrieved content, and output. A larger window helps only when the added context is relevant and governed.

Question 24

Embeddings

Accepted Answer

Embeddings convert text, images, or other content into numeric vectors that capture meaning. Similar vectors support semantic search, recommendations, and RAG retrieval.

Question 25

Hallucination

Accepted Answer

A hallucination is a fluent or confident model response that is unsupported, incorrect, or fabricated. Grounding, evaluation, guardrails, and human review reduce the risk.

Question 26

Prompt engineering

Accepted Answer

Prompt engineering designs the instructions, context, examples, constraints, and output format given to a model. It improves behavior without changing the model weights.

Question 27

Zero-shot vs few-shot prompting

Accepted Answer

Zero-shot prompting gives instructions without examples. Few-shot prompting includes examples that show the desired pattern, tone, structure, or decision rule.

Question 28

Temperature

Accepted Answer

Temperature controls output randomness. Lower settings usually make responses more predictable; higher settings can increase variety but may reduce consistency.

Question 29

Top-p

Accepted Answer

Top-p controls sampling by limiting generation to a cumulative probability mass of likely tokens. Adjust it deliberately when balancing variety and predictability.

Question 30

Maximum tokens

Accepted Answer

Maximum tokens limits how much output a generative model can produce. It helps control cost, latency, verbosity, and whether responses fit the application experience.

Question 31

Amazon Bedrock

Accepted Answer

Amazon Bedrock is AWS's managed service for building generative AI applications with foundation models. It supports model access, customization paths, RAG, agents, guardrails, and evaluation patterns.

Question 32

RAG

Accepted Answer

Retrieval-Augmented Generation retrieves relevant trusted content and gives it to a foundation model as context. It is useful for current, private, or source-cited knowledge.

Question 33

Bedrock Knowledge Bases

Accepted Answer

Knowledge Bases for Amazon Bedrock helps implement RAG by connecting source documents, embeddings, vector storage, retrieval, and model responses in a managed workflow.

Question 34

RAG vs fine-tuning

Accepted Answer

Use RAG when facts change often, source grounding is needed, or enterprise documents should stay outside model weights. Use fine-tuning for task behavior, style, or pattern adaptation.

Question 35

Bedrock Agents

Accepted Answer

Agents for Amazon Bedrock help a foundation model plan steps, use instructions, retrieve context, and invoke approved tools or APIs to complete a task.

Question 36

Bedrock Guardrails

Accepted Answer

Guardrails for Amazon Bedrock help apply configured controls for safety, denied topics, grounding, word filters, and sensitive information handling in supported GenAI workflows.

Question 37

Amazon Q Business

Accepted Answer

Amazon Q Business is a managed generative AI assistant for enterprise knowledge and work. It is different from building a custom Bedrock application from scratch.

Question 38

Amazon Q Developer

Accepted Answer

Amazon Q Developer supports software development tasks such as code suggestions, code explanation, security-related checks, and developer assistance in supported tools.

Question 39

Amazon SageMaker AI

Accepted Answer

Amazon SageMaker AI is the broader managed service family for building, training, deploying, and operating ML models when a team needs custom ML lifecycle capabilities.

Question 40

SageMaker Canvas

Accepted Answer

SageMaker Canvas gives business users a visual, no-code way to explore and build ML models, while more technical SageMaker tools support deeper ML workflows.

Question 41

Amazon Textract

Accepted Answer

Amazon Textract extracts printed text, handwriting, forms, and tables from documents. It fits document capture and extraction before later analysis or review.

Question 42

Amazon Comprehend

Accepted Answer

Amazon Comprehend analyzes text for language, entities, sentiment, key phrases, topics, and related NLP insights. It works on text that has already been captured.

Question 43

Transcribe, Polly, and Translate

Accepted Answer

Amazon Transcribe converts speech to text, Amazon Polly converts text to speech, and Amazon Translate converts text between languages.

Question 44

Rekognition vs Textract

Accepted Answer

Amazon Rekognition analyzes images and video for visual content. Amazon Textract focuses on extracting structured text and data from documents.

Question 45

Fairness

Accepted Answer

Fairness means the AI system should avoid unjustified harm or discrimination across affected groups. It requires data review, evaluation, process design, and ongoing monitoring.

Question 46

Transparency and explainability

Accepted Answer

Transparency tells people how AI is being used and what limits apply. Explainability helps stakeholders understand why a prediction or recommendation occurred.

Question 47

Human review

Accepted Answer

Human review is important when AI affects customers, regulated decisions, safety, finances, employment, health, legal outcomes, or brand risk. Reviewers need authority and escalation paths.

Question 48

SageMaker Clarify

Accepted Answer

SageMaker Clarify supports ML bias detection and explainability. It can provide evidence for responsible AI review, but governance teams still set policy thresholds.

Question 49

Shared responsibility for AI

Accepted Answer

AWS secures the cloud infrastructure. Customers remain responsible for identities, data, prompts, application behavior, allowed use, logging choices, and business governance.

Question 50

AI cost controls

Accepted Answer

AI cost can come from tokens, model choice, endpoints, training jobs, vector storage, logs, data movement, and human review. Use budgets, tags, alerts, quotas, and right-sized models.

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