AIF-C01 · Mock exam · 25 questions
AIF-C01 mock exam: 25 free practice questions
A free AIF-C01 practice test with 25 exam-style questions drawn from the whole course and weighted by domain like the exam guide. Answer at your pace and read why each option is right or wrong.
- 65Questions
- 700 (scaled 100–1,000)Passing score
- Fundamentals of AI and ML 20%
- Fundamentals of GenAI 24%
- Applications of Foundation Models 28%
- Guidelines for Responsible AI 14%
- Security, Compliance, and Governance for AI Solutions 14%
Exam details from the vendor’s exam guide, captured 2026-08-30.
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Q1 / 25
A banking assistant receives user text that says to ignore its developer instructions and act as a chef. No training or reference data changes, and no confidential text is requested. Which risk best describes the attempt?
- APoisoning of the training dataset
- BJailbreaking of native safety controls
- CHijacking through prompt injection
- DExposure through prompt leakage
Show the answer
The user is trying to override the intended role and redirect behavior at inference time, which is the defining pattern of hijacking.
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Q2 / 25
At what stage does context engineering primarily change what a model receives?
- ADuring model pretraining
- BWhile retrieval searches for external material
- CAt inference time for a request
- DWhile memory stores earlier interaction state
Show the answer
The practice changes the information supplied with a request at inference time, leaving the model's learned parameters unchanged.
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Q3 / 25
A team must score hundreds of generated summaries against approved reference summaries. The result must be deterministic, and the team specifically wants lexical coverage rather than contextual similarity. Which metric best fits?
- AA second language model that scores and explains each summary
- BBERTScore applied to contextual embeddings of both summaries
- CHuman review focused on coherence and user experience
- DROUGE applied to generated and reference summaries
Show the answer
ROUGE directly measures n-gram overlap for reference-based summarization, matching both the deterministic and lexical requirements.
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Q4 / 25
What is the role of SageMaker JumpStart in generative AI development?
- AA model hub for evaluating, selecting, customizing, and integrating models
- BA managed runtime for isolating and scaling agent sessions
- CA conversational suite for analytics, workflows, apps, and research
- DAn agentic IDE that turns specifications into implementation plans
Show the answer
JumpStart provides built-in public and proprietary models inside SageMaker AI; AgentCore, Kiro, and Quick fulfill different responsibilities.
Next → 4 / 25 -
Q5 / 25
Which metric measures the share of all classification predictions that are correct?
- AAccuracy across the complete prediction set
- BPrecision among the predicted positive cases
- CCost per user across the served population
- DRecall among the actual positive cases
Show the answer
Accuracy uses all predictions as its scope; precision and recall use positive-case denominators, while cost per user is a business metric.
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Q6 / 25
Which prompt component tells the model what action to perform?
- AThe negative prompt
- BThe instruction
- CTask context
- DThe demonstration count
Show the answer
An instruction expresses the requested task, such as classifying, summarizing, or answering a question.
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Q7 / 25
What determines inference cost under a token-based pricing model?
- AThe number of model providers listed in the service catalog
- BThe displayed paragraph count
- CThe number of input and output tokens processed
- DThe number of evaluation criteria written before deployment
Show the answer
Token-based pricing maps cost to processed input and output tokens rather than document layout or catalog size.
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Q8 / 25
Which statement correctly compares data properties?
- ALabeling describes whether targets are supplied, while structure describes whether data follows a fixed schema
- BLabeling describes rows and columns, while structure describes whether targets are correct
- CLabeling describes chronological order, while structure describes whether an image contains a caption
- DLabeling describes text modality, while structure describes whether observations have timestamps
Show the answer
Label state, structural organization, modality, and temporal order are separate axes, so one dataset can carry several descriptions simultaneously.
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Q9 / 25
Which cost placement best distinguishes pre-training from request-time guidance?
- APre-training adds examples to each request without training
- BPre-training incurs model-training work before deployment
- CPre-training retrieves private chunks only during inference
- DPre-training creates no upfront work and only caches prompts
Show the answer
Pre-training places customization work in training the model rather than merely adding guidance to individual requests.
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Q10 / 25
Why are hallucinations a distinct risk from biased outputs?
- AHallucinations concern protected works; bias concerns excessive computational resource use.
- BHallucinations concern customer relationships; bias concerns human approval before release.
- CHallucinations concern model reuse; bias concerns data storage and processing efficiency.
- DHallucinations concern fabricated or incorrect information; bias concerns disparities across groups.
Show the answer
The classification follows the failure: factual fabrication indicates hallucination, while unequal group behavior indicates bias.
Next → 10 / 25 -
Halfway, at your pace
In the app the mock exam is timed and scored like the real thing.
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Q11 / 25
Which Amazon Quick feature grounds responses in an organization's connected documents and data sources?
- AAmazon Quick Index
- BAmazon Quick Flows
- CAmazon Quick Sight
- DAmazon Quick Research
Show the answer
Quick Index connects organizational information for grounded responses; Sight visualizes data, Flows automates tasks, and Research produces cited research reports.
Next → 11 / 25 -
Q12 / 25
Which value category is demonstrated when a risk score is presented to an investigator who makes the final decision?
- AAssisting human decision making with predictive evidence
- BScaling identical manual reviews without a learned pattern
- CGuaranteeing a prescribed outcome through fixed logic
- DReplacing the investigator with an exact calculation
Show the answer
Decision assistance uses a prediction as evidence while the person remains responsible for the action.
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Q13 / 25
What is the expected response effect of increasing temperature?
- ALower-probability token choices become more likely
- BThe maximum output-token count is reduced
- CThe submitted context becomes shorter
- DGeneration stops at a specified character sequence
Show the answer
Higher temperature flattens the probability distribution and increases randomness in token selection.
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Q14 / 25
A service describes its capabilities before use, but users with accessibility needs struggle at input, processing appears frozen, and completed results cannot be challenged. Which redesign addresses the full journey?
- AExpand the capability description and leave processing plus post-output interaction unchanged for affected users.
- BImprove accessible input guidance, add progress information, and provide explanation plus feedback controls.
- CAdd performance metrics before use and prevent challenges to completed outputs.
- DAdd a risk rating after output but keep input inaccessible and processing opaque.
Show the answer
The failures occur at initial interaction, processing, and post-interaction, so the design must address accessibility, activity visibility, and response mechanisms at those stages.
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Q15 / 25
A team runs an AI application on Amazon EC2 and installs its own inference software. It must assign responsibility for operating-system patches and application updates. Who owns both tasks?
- AThe customer, because both layers run inside its EC2 workload
- BAWS, because EC2 transfers guest software maintenance to the provider
- CThe external auditor, because patching is a compliance control
- DAWS for the application and the customer for physical infrastructure
Show the answer
On EC2, AWS protects the underlying infrastructure while the customer maintains the guest operating system and installed applications.
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Q16 / 25
Why can adding unrelated material weaken a request context?
- AIt permanently replaces the model's learned parameters
- BIt makes retrieval unnecessary for every application task
- CIt converts conversation state into learned parameters
- DIt can compete with the task-relevant information
Show the answer
Useful context is selected for relevance and sufficiency; volume alone does not make information useful for the task.
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Q17 / 25
A contact center must turn a live audio stream into a real-time text transcript. Which service should it use?
- AAmazon Transcribe, because it accepts live audio and streams transcripts
- BAmazon Comprehend, because it accepts live audio and streams transcripts
- CAmazon Polly, because it accepts live audio and streams transcripts
- DAmazon Translate, because it accepts live audio and streams transcripts
Show the answer
Amazon Transcribe directly supports live audio input and real-time transcript output, matching both the data type and latency requirement.
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Q18 / 25
A chat assistant answers well, but its serving cost and latency at peak traffic are unacceptable, and leadership wants the replacement to behave as closely as it can to the current model. The team accepts one extra training stage. Which approach fits?
- AContinue pre-training the current model with newly collected traffic
- BDistill the current model into a smaller student trained on its responses
- CInstruction tune the current model on labeled prompt-and-output pairs
- DFine-tune the current model once more on a smaller and more task-specific dataset
Show the answer
One option changes the size and speed profile of what gets served, and the rest keep the same model in place. Distillation exists to create a smaller, more efficient model that mimics the behavior of a larger, more advanced one, which is what the closeness constraint asks for.
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Q19 / 25
What does AI decision transparency provide to a user?
- AInformation about system activity, confidence, and the basis for an output
- BA channel that collects challenges without explaining the decision
- CA performance score with no context about model behavior
- DA guarantee that every model output is factually correct and harmless for every request
Show the answer
Decision transparency helps a user understand what the system is doing, how confident it is, and what influenced its output.
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Q20 / 25
Which IAM feature gives an AWS workload temporary credentials without distributing long-term IAM user credentials?
- AAn IAM role assumed by the workload
- BA Macie sensitive-data discovery job
- CA PrivateLink endpoint policy used as a password
- DAn IAM policy stored as the workload credential
Show the answer
A role supplies temporary credentials to the workload; policies define authorization after an identity is established.
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Q21 / 25
Which generative AI advantage supports different tasks or contexts through supplied input and configuration?
- AAdaptability
- BInaccuracy
- CNondeterminism
- DInterpretability limitation
Show the answer
Adaptability concerns serving varied use cases, whereas the other options describe limitations or risks.
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Q22 / 25
A finance team has two needs: flag suspicious card transactions now and estimate monthly transaction volume next year. Which mapping is correct?
- AFraud detection for flags; forecasting for future volume
- BSpeech recognition for flags; NLP for future volume
- CForecasting for flags; computer vision for future volume
- DKnowledge retrieval for flags; clustering for future volume
Show the answer
Fraud detection evaluates suspicious events, while forecasting estimates a future time-series outcome.
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Q23 / 25
A RAG assistant begins returning attacker-controlled claims after a newly added repository document is retrieved. The system prompt remains secret, and users are not overriding instructions. Which diagnosis matches both the affected asset and stage?
- APrompt exposure that reveals developer instructions
- BGoal hijacking that redirects the system instruction
- CReference-data poisoning that influences grounded responses
- DJailbreaking that bypasses native content moderation
Show the answer
The malicious change entered the reference repository and then influenced generation through retrieval, which identifies poisoning rather than an inference-only prompt attack.
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Q24 / 25
A classifier performs poorly on training data and evaluation data. The team confirms that labels are reliable and that demographic subgroup metrics are comparable. It must diagnose model fit before changing production monitoring. Which diagnosis best fits?
- AOverfitting because the model memorizes training data but cannot generalize
- BUnderfitting because the model fails to capture the training relationship
- CBias drift because a live metric exceeded its configured baseline threshold
- DLabel corruption because actual outcomes cannot be compared with predictions
Show the answer
Poor training performance is the decisive sign of underfitting after label and subgroup issues have been ruled out.
Next → 24 / 25 -
Q25 / 25
An organization must process model requests inside the EU and uses a Service Control Policy that permits only its source Region. Calls made through the EU geographic inference profile start to fail. What explains the failure and resolves it?
- AThe data no longer resides in the source Region, so it has to be replicated to every destination Region before calls succeed
- BEncryption in transit is blocking the routed requests, so it has to be disabled between the source and destination Regions
- CThe profile can route to other destination Regions, so the policy must allow them or carry an inference-profile exception
- DGeographic profiles send data outside the EU, so the workload has to stop using cross-Region inference to stay compliant
Show the answer
A geographic profile stays inside its geography but still spreads work across several Regions inside it, so a Regional allow list has to account for the destination Regions the profile can use.
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That’s the whole mock exam
Every question you miss comes back exactly when you’re about to forget it.
How to use this mock exam
Sit all 25 questions in one go: the mix covers every domain in the same proportion as the exam, so a low score points at the domain you skipped rather than at bad luck.
Read the explanation under every question, including the ones you got right — the reason an option is wrong is usually the thing being tested.
Then retake it in the app, where the mock exam is timed and scored and the questions you miss come back on a schedule.
The whole course, on your phone
Lessons you can read, audio you can listen to on the way to work, and practice that remembers what you got wrong.