AI0-001 Practice Exam 61 — 20 Questions

Question 1

A retail company runs a customer-facing chatbot backed by a large language model. The chatbot has access to a tool that looks up order status by order ID. A penetration tester finds that by typing a crafted sentence, a user can make the chatbot call the order-status tool with an arbitrary order ID belonging to another customer and read the response. Which control most directly prevents this unauthorized tool invocation?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Enforce authorization checks in the tool backend so it only returns orders belonging to the authenticated user.. The flaw is that the tool performs a lookup based solely on a model-supplied order ID, with no check that the order belongs to the authenticated user. Enforcing authorization in the tool backend removes trust from the model and blocks cross-customer access regardless of how the prompt is crafted. Prompt instructions, temperature changes, and after-the-fact logging do not establish that access boundary.

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Add a system prompt instructing the model never to reveal other customers' order details.

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Increase the model's temperature setting to make its responses less predictable.

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Log every tool call the chatbot makes and review the logs daily for suspicious order IDs.

Question 2

A retail company runs a demand-forecasting model in production. Over three weeks, the average order value of incoming transactions has risen by 40 percent because of a promotional campaign, and forecast error has grown steadily. The model was trained on twelve months of historical data with no promotion periods. Which action should the operations team take FIRST to restore forecast reliability?

Accepted Answer

Instrument input-feature drift monitoring and retrain or recalibrate the model with recent promotion-period data.. The rising average order value from the promotion changed the distribution of input features relative to the training set, which is classic data drift and explains the growing forecast error. Monitoring feature distributions detects the shift, and retraining or recalibrating with promotion-period data realigns the model with current conditions. Throughput, caching, and threshold changes do not alter the underlying statistical mismatch.

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Increase the inference batch size so the model processes more transactions per call.

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Add a caching layer in front of the inference endpoint to reduce repeated calls.

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Lower the model's confidence threshold so more forecasts are emitted per period.

Question 3

A media company trains a video tagging model on a large dataset in the cloud. The model will run inference on-premises in a facility with intermittent network connectivity, and the operations team wants to avoid re-authoring the model for each target runtime. Which deployment artifact best meets these constraints?

Accepted Answer

An ONNX model executed with the ONNX Runtime. ONNX defines a standardized computation graph, and ONNX Runtime executes that graph across platforms and hardware without requiring the original training framework. Exporting to ONNX gives the media company a single artifact that runs on-premises in a disconnected facility and removes the need to re-author the model for each inference runtime, satisfying both stated constraints.

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A pickle file of the scikit-learn estimator

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The native TensorFlow SavedModel directory

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A Docker image containing the full training environment

Question 4

A software company wants to add a feature that automatically transcribes customer support phone calls into text for analysis. Which type of AI technology is best suited for this task?

Accepted Answer

Automatic speech recognition. Automatic speech recognition (ASR) is the AI technology that converts spoken language into text. It is the foundational component for transcribing phone calls, enabling subsequent analysis. Computer vision handles images, NLP processes text after transcription, and reinforcement learning is for sequential decision-making, so none of those directly perform the speech-to-text conversion required.

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Computer vision

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Natural language processing

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Reinforcement learning

Question 5

A financial services firm runs a credit-scoring AI model in production. The compliance team wants continuous assurance that the deployed model still meets performance and fairness expectations as customer behavior changes. Which TWO operational practices best support this goal? (Choose two.)

Accepted Answer

Scheduled monitoring of key performance metrics such as AUC, precision, and recall against recent labeled outcomes. Continuous assurance requires active measurement of the deployed system on two fronts: predictive performance against fresh labeled outcomes and fairness across protected groups. These two practices detect degradation as conditions change and generate evidence for compliance. Retraining without evidence, enlarging the model, or merely archiving requests do not by themselves confirm that the model remains effective and equitable.

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Retraining the model on the full historical dataset every night regardless of any detected change

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Storing all inference requests and responses in immutable logs for later forensic analysis

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Increasing the model's parameter count to improve its capacity to fit customer data

Question 6

A media company exposes a text-to-image generation API built on a diffusion model. Users submit prompts and receive generated images. The security team wants to reduce the risk that the API can be abused to produce prohibited content such as realistic depictions of public figures in compromising situations. (Choose two.)

Accepted Answer

Deploy a prompt classifier that blocks or rewrites requests matching known prohibited-content patterns before generation.. Reducing abuse of a generative API requires controls that act on the content itself. A prompt classifier blocks or rewrites harmful requests before generation, and an output safety classifier catches disallowed images that still emerge. Together they provide input and output guardrails, while transport security, rate limiting, and insecure retention address different concerns and leave the content-abuse risk largely unmitigated.

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Store all generated images indefinitely in an unencrypted bucket for later review by the security team.

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Rate-limit each API key to a fixed number of image generations per minute.

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Enable TLS 1.3 for all API connections and require client certificates for authentication.

Question 7

A healthcare analytics company has trained an AI model to predict patient readmission risk using a dataset that includes ZIP code, race, and historical healthcare costs. Before deployment, the compliance team runs a fairness audit and finds that the model's predictions correlate strongly with race even though race was not used as a direct input feature. Which of the following BEST describes this phenomenon?

Accepted Answer

Proxy discrimination, where a seemingly neutral feature acts as a substitute for a protected attribute.. The model never receives race directly, but ZIP code and historical cost encode race-related disparities from decades of unequal healthcare access and residential segregation. This is proxy discrimination, and it defeats naive fairness approaches that only drop the protected column. Effective mitigation requires measuring disparate impact and auditing correlated features, because removing the explicit attribute does not remove its statistical footprint.

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Overfitting, because the model memorized race-related patterns in the training set instead of generalizing.

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Label leakage, because the target variable inadvertently encodes race information.

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Data drift, because patient demographics changed after the model was trained.

Question 8

A municipality uses an AI system to triage requests for public housing assistance. Community advocates ask how they can challenge a denial that they believe resulted from an erroneous data match. Which governance mechanism is MOST appropriate to provide affected individuals with a route to contest automated outcomes?

Accepted Answer

An appeals process that lets applicants request human review and correction of the data and logic behind a denial.. Contestability requires an actionable process through which an affected person can dispute an automated decision, obtain human review, and have errors corrected. An appeals process with human reconsideration provides that route. Impact assessments, model cards, and fairness audits improve transparency and systemic oversight but do not give individuals a means to challenge their own outcomes.

Answer

A quarterly fairness audit that measures disparate impact across demographic groups in the triage outcomes.

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A model card that documents the triage model's training data, performance metrics, and known limitations.

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A published algorithmic impact assessment summarizing the system's risks and mitigations.

Question 9

A retail bank is deploying a customer-facing AI assistant that must never disclose internal policy text. The team has a system prompt with instructions, but red-team testing shows users can extract the policy by asking the model to 'repeat everything above this line.' Which implementation change most directly mitigates this prompt-injection extraction risk in production?

Accepted Answer

Add an input guardrail that detects and blocks prompt-extraction patterns before the request reaches the model.. The extraction attack succeeds because untrusted user input is concatenated with trusted instructions and the model follows the most recent instruction. Placing a guardrail in front of the model detects and blocks known extraction phrasing, which is the most direct runtime mitigation. Temperature, fine-tuning, and context size do not intercept the malicious instruction before it is processed.

Answer

Reduce the context window size so there is less room for the model to repeat the system prompt.

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Move the policy text from the system prompt into the fine-tuning dataset so the model learns it as weights.

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Increase the model temperature so responses become less deterministic and harder to reverse engineer.

Question 10

A small e-commerce startup has only 800 labeled customer-support tickets and needs to classify new tickets into categories such as billing, shipping, and returns. The team has no budget for large-scale annotation and wants to leverage a model already trained on millions of general text documents. Which approach best fits this constraint?

Accepted Answer

Fine-tune a pretrained language model on the 800 labeled tickets for the classification task.. Fine-tuning a pretrained language model is ideal when labeled data is scarce, because the model already encodes general language knowledge and only needs adaptation to the ticket categories. This approach uses the 800 labels efficiently and outperforms training from scratch, rule-based matching, or unsupervised clustering.

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Use a rule-based keyword matcher that assigns categories based on the presence of predefined terms.

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Apply k-means clustering to the ticket text and label each cluster with the most frequent category.

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Train a transformer from scratch on the 800 tickets using a high learning rate.

Question 11

A software company is developing an AI-powered code generation tool that suggests code snippets to developers. The company wants to align with the EU AI Act's transparency requirements. Which two actions should the company take? (Choose two.)

Accepted Answer

Provide documentation to developers about the AI system's capabilities and limitations. The EU AI Act's transparency obligations for AI systems that interact with users require clear disclosure that the user is interacting with an AI, and for providers to supply instructions for use that describe capabilities and limitations. For a code generation tool, informing developers that suggestions are AI-generated and documenting the tool's limitations fulfills these duties.

Answer

Publish the full training dataset used to train the code generation model

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Ensure the AI system marks AI-generated code with a machine-readable watermark

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Obtain explicit consent from every developer before they use the tool

Question 12

A logistics company is deploying an AI model that predicts delivery delays. The model will run on edge devices in trucks with intermittent connectivity. The team must ensure the deployment meets latency and reliability requirements. Which TWO implementation practices are MOST appropriate for this edge AI deployment? (Choose two.)

Accepted Answer

Quantize the model to a smaller numeric precision so it fits the device's memory and compute budget.. Edge deployment under intermittent connectivity requires the model to run locally and be small enough for the device. Quantization reduces size and compute, and local inference with store-and-forward caching keeps predictions available offline while queuing results for later sync. Cloud routing, larger models, and disabled monitoring all conflict with the latency, memory, and reliability constraints described.

Answer

Disable model monitoring on the device to reduce CPU overhead and extend battery life.

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Increase the model's parameter count so it can learn more complex delay patterns from historical data.

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Route every prediction request to the cloud so the model always uses the newest weights.

Question 13

A research lab is training a large language model on a cluster of GPUs. They notice that training throughput decreases significantly when scaling from 8 to 16 GPUs. The model uses data parallelism with synchronous updates. Which factor is most likely causing the decreased throughput?

Accepted Answer

Increased communication overhead for gradient synchronization across GPUs.. Synchronous data parallelism requires all-reduce operations to synchronize gradients. As the number of GPUs increases, the communication volume and frequency grow, and if the interconnect bandwidth is limited, this becomes a bottleneck. This is a common scaling challenge in distributed training, leading to sublinear speedup or even decreased throughput.

Answer

The learning rate is too high for the larger effective batch size.

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The model is not using mixed precision training.

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Insufficient GPU memory causing out-of-memory errors.

Question 14

A retail bank deploys an AI model that approves or declines small-business loan applications. Regulators require the bank to explain any adverse decision to the applicant in plain language. The model is a gradient-boosted ensemble over dozens of features, and the bank's data scientists cannot easily describe why a specific applicant was declined. Which approach best satisfies the regulatory requirement?

Accepted Answer

Apply a local explanation technique such as SHAP values to identify the features that most influenced the individual applicant's score and translate them into plain-language reasons.. Regulatory adverse-action requirements demand case-specific reasons for each declined applicant. Local explanation techniques such as SHAP values attribute an individual prediction to its contributing features, which can then be rendered in plain language. Global feature importance, aggregate audit results, and swapping in a simpler model either describe the population rather than the individual or disrupt the production system without addressing the disclosure need.

Answer

Publish the model's global feature importance rankings alongside each decision notice so applicants see which factors matter most overall.

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Replace the gradient-boosted ensemble with a single decision tree so the decision path itself can be shown to the applicant.

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Provide applicants with the model's overall accuracy and fairness audit results to demonstrate that the system is trustworthy.

Question 15

A fraud detection team trains a gradient boosted tree model on transaction data. During evaluation, the team notices the model performs extremely well on the training set but poorly on a holdout set drawn from the same time period. Investigation shows that a feature named 'chargeback_flag' is populated only after a dispute is resolved, sometimes weeks after the transaction. The team wants to deploy the model to score transactions in real time. Which action best addresses the problem?

Accepted Answer

Remove the 'chargeback_flag' feature and retrain the model using only features that are available at transaction scoring time.. The 'chargeback_flag' is populated only after a dispute is resolved, which is after the transaction outcome is known, so it leaks the label into training. The correct fix is to identify features that are actually available at real-time scoring and retrain without the leaky field. Hyperparameter tuning, larger validation sets, or partial replacements do not remove the leak and will keep offline metrics misleadingly high.

Answer

Increase the size of the holdout set so the evaluation becomes more statistically reliable.

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Replace the flag with a rolling average of the customer's past chargebacks to preserve some of its signal.

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Apply stronger regularization and reduce the model's maximum depth to prevent it from relying on the flag.

Question 16

A logistics company runs an AI route-optimization model on a cloud inference endpoint. The model receives 200 requests per second during business hours and 20 requests per second at night. The operations team wants to reduce cost without violating the 200 ms p95 latency SLA, and they observe that provisioned capacity is sized for peak load. Which approach is MOST appropriate?

Accepted Answer

Configure scheduled scaling that reduces replica count during known low-traffic windows and scales up before peak hours.. When traffic follows a predictable daily curve, scheduled scaling that shrinks replicas overnight and pre-scales before the morning peak directly removes the cost of idle peak-sized capacity while protecting the latency SLA. Serverless cold starts, feature reduction, and regional relocation do not address the overprovisioning root cause and risk either SLA violations or business degradation.

Answer

Move the endpoint to a region with lower compute pricing and keep the same replica count.

Answer

Reduce the model's input feature set to lower per-request compute time.

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Switch to a serverless inference endpoint with a cold-start penalty of several seconds per invocation.

Question 17

A healthcare technology company is preparing to deploy an AI system that analyzes patient X-rays to detect early-stage lung cancer. The system is intended to be marketed as a medical device in the European Union. Under the EU AI Act, which classification and corresponding obligation apply to this system?

Accepted Answer

It is a high-risk AI system and must comply with requirements for risk management, data governance, technical documentation, and human oversight before being placed on the market.. The EU AI Act classifies AI systems used in medical devices as high-risk under Annex III. This triggers a comprehensive set of obligations including risk management, data governance, technical documentation, and human oversight. The system is not prohibited, nor is it limited or minimal risk, because its output directly affects patient diagnosis and safety.

Answer

It is a limited-risk AI system and only needs to provide transparency notices to patients about its use.

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It is a minimal-risk AI system and can be deployed without any additional regulatory requirements beyond existing medical device regulations.

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It is a prohibited AI system because it uses subliminal techniques to manipulate patient behavior.

Question 18

A hospital is deploying an AI triage assistant that suggests priority levels for emergency room patients. Clinicians will review every suggestion before acting. The compliance team requires that the system log who reviewed each suggestion, what the clinician decided, and whether they overrode the AI. Which implementation practice best satisfies this requirement?

Accepted Answer

Record a human-in-the-loop audit trail that captures the AI suggestion, the clinician's decision, and any override reason.. Human oversight in high-stakes AI requires evidence that a qualified person reviewed each suggestion and retained authority to disagree. An audit trail that links the suggestion, the human decision, and the override rationale provides that evidence and supports continuous monitoring. Auto-acceptance, model-only logging, and delayed batch feedback all fail to document meaningful human involvement.

Answer

Store clinician feedback in a separate quality-improvement database that is refreshed monthly.

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Enable automatic acceptance of AI suggestions when the model's confidence score exceeds 0.95.

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Log only the model's input features and output priority so that predictions can be reproduced later.

Question 19

A retail company uses an AI system to detect shoplifting from surveillance footage. The system has been criticized for disproportionately flagging customers from certain ethnic groups. The company wants to address this ethical concern. Which of the following should be the first step?

Accepted Answer

Conduct a bias audit to quantify disparities across demographic groups.. The first step in addressing bias is to measure it. A bias audit provides data on how the system performs across different groups, which is essential for understanding the problem and designing effective mitigation. Without this assessment, any corrective action is likely to be guesswork. This approach is consistent with responsible AI practices.

Answer

Immediately disable the AI system and revert to manual monitoring.

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Retrain the model with a more diverse dataset without analyzing the current bias.

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Publish a public apology and promise to fix the issue.

Question 20

A financial analyst is using a linear regression model to predict housing prices based on square footage. The model's predictions are consistently off by a large margin for both very small and very large houses, while performing well for average-sized houses. Which phenomenon is most likely occurring?

Accepted Answer

Non-linearity in the relationship. The model's errors are systematic at the extremes of the predictor range, which is a hallmark of assuming a linear relationship when the true relationship is non-linear. A linear regression cannot bend to fit curved patterns, so it underfits the tails. Overfitting would show high variance, underfitting would show poor fit everywhere, and multicollinearity requires multiple correlated predictors.

Answer

Underfitting

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Multicollinearity

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Overfitting