AI0-001 Practice Exam 56 — 20 Questions

Question 1

A financial institution is implementing an AI-based fraud detection system. The compliance officer is concerned about potential bias in the model that could lead to unfair treatment of certain customer groups. Which governance practice should be prioritized to address this concern?

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

Accepted Answer

Schedule regular bias audits using fairness metrics.. Regular bias audits using fairness metrics (Option B) are the correct governance practice because they provide a systematic, quantitative method to detect and measure disparate impact across protected groups. Unlike simply collecting more data, audits directly evaluate model outputs for statistical parity, equal opportunity, or other fairness definitions, enabling the institution to identify and remediate bias proactively. This aligns with regulatory expectations for ongoing monitoring and accountability in AI governance.

Answer

Increase the diversity of the training data by collecting more samples from underrepresented groups.

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Retrain the model every month with the latest transaction data.

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Use SHAP values to provide explanations for each prediction.

Question 2

A healthcare startup is building a diagnostic support system using a large language model. The system must provide accurate, evidence-based answers and avoid generating harmful or fabricated information. Which THREE techniques should be implemented to achieve this? (Choose 3)

Accepted Answer

Retrieval-Augmented Generation (RAG). Option A (Retrieval-Augmented Generation, RAG) is correct because grounding the LLM's responses in an external, authoritative medical knowledge base (e.g., PubMed, clinical guidelines) at inference time supplies verifiable evidence and sharply reduces hallucination compared to relying on parametric memory alone. Option C (chain-of-thought prompting) is correct because eliciting intermediate reasoning steps improves the model's accuracy on complex diagnostic questions and makes its conclusions auditable, which supports evidence-based clinical decision support. Option E (fine-tuning on medical textbooks and guidelines) is correct because domain-specific supervised fine-tuning adapts the model's weights to accurate, curated medical content and terminology, raising factual reliability for the healthcare domain. Option B does not belong because disabling output filtering removes safety guardrails and increases the risk of harmful content, the opposite of the stated requirement. Option D does not belong because raising the temperature increases sampling randomness and creativity, which promotes fabricated or inconsistent answers rather than accurate, evidence-based ones.

Answer

Disabling output filtering to speed up generation

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Increasing the temperature parameter to encourage creativity

Question 3

During feature engineering, a data scientist creates a new feature that is a linear combination of two existing features. What risk does this pose to the model?

Accepted Answer

Multicollinearity. Creating a new feature as a linear combination of two existing features introduces perfect multicollinearity, where the new feature is an exact linear function of the original ones. This violates the assumption of no perfect multicollinearity in linear models, causing the design matrix to become singular and making coefficient estimates unstable or impossible to compute. Even in non-linear models, high multicollinearity can inflate variance and reduce interpretability.

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Data leakage

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Overfitting

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Underfitting

Question 4

A data scientist fine-tunes a large language model for a legal document summarization task. After fine-tuning, the model performs well on test data but produces summaries that include hallucinated legal clauses. Which mitigation strategy is most effective?

Accepted Answer

Implement retrieval-augmented generation (RAG) to provide factual context.. Implement retrieval-augmented generation (RAG) to provide factual context. RAG reduces hallucinations by allowing the model to retrieve relevant, factual information from an external knowledge base during generation, grounding its output in verified data. Option A (different tokenizer) does not address the core issue of factual accuracy. Option B (decrease temperature) affects randomness but does not prevent the model from fabricating content. Option D (max token limit) truncates output but does not stop the model from including false information within that limit.

Answer

Use a different tokenizer during fine-tuning.

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Decrease the temperature parameter to 0.1 during inference.

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Set a maximum token limit of 50 for each summary.

Question 5

Which TWO of the following are effective techniques for detecting bias in an AI model?

Accepted Answer

Fairness metrics such as equal opportunity difference. Option A, fairness metrics such as equal opportunity difference, is correct because it quantitatively compares true positive rates across protected groups, directly exposing unequal model performance that indicates bias. Option E, disparate impact analysis, is correct because it applies the four-fifths (80%) rule to compare selection or approval rates between groups, a standard legal and statistical method for detecting discriminatory outcomes. Options B, C, and D are not marked correct: feature importance scores only show which inputs influence predictions and do not by themselves reveal bias against a group; a confusion matrix on the entire dataset aggregates all groups and hides per-group disparities unless disaggregated; and cross-validation accuracy measures overall generalization performance, not fairness across subgroups.

Answer

Feature importance scores

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Confusion matrix on the entire dataset

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Cross-validation accuracy

Question 6

A team is fine-tuning a BERT model for a document classification task. They notice the model achieves high F1 scores on the training set but low F1 on the validation set. Which regularization technique would be MOST effective?

Accepted Answer

Dropout. Dropout is the most effective regularization for transformer-based models like BERT because it randomly deactivates neurons during training, forcing the network to learn redundant representations and preventing co-adaptation. BERT already includes dropout layers in its architecture (attention dropout, hidden dropout), and increasing or tuning the dropout rate directly addresses overfitting on the training set.

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L1 regularization

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L2 regularization

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Reduce batch size

Question 7

An AI security engineer is hardening an LLM application against prompt injection. Which TWO controls are most effective? (Select two.)

Accepted Answer

Training the model with adversarial examples of prompt injection. Option B is correct because training the model with adversarial examples of prompt injection (adversarial training) exposes it to malicious inputs during fine-tuning, helping it learn to recognize and resist injection attempts rather than comply with them. Option C is correct because input sanitization that strips special characters and known injection patterns (e.g., delimiter tokens, instruction-override phrases) removes or neutralizes the attack surface before the prompt reaches the model, providing a deterministic defense layer. Option A is not the best choice because fine-tuning on safe responses teaches desired output style but does not specifically teach the model to detect or refuse injection attempts, so it offers weak protection against adversarial inputs. Option D is incorrect because increasing temperature makes outputs more random and less predictable, which does not improve security and can even worsen reliability. Option E is incorrect because using a smaller model for faster inference addresses latency and cost, not prompt-injection resistance, and smaller models are often more vulnerable.

Answer

Fine-tuning the model on a dataset of safe responses

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Increasing the model's temperature setting

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Using a smaller model for faster inference

Question 8

A team is deploying a fine-tuned LLM for code generation. They need to ensure the model output is always valid JSON. Which prompt engineering technique should they use?

Accepted Answer

Few-shot examples of valid JSON outputs. Few-shot examples of valid JSON outputs condition the model on the exact schema, key names, and formatting it should produce, dramatically increasing the probability of schema-conformant output. Because LLMs are next-token predictors, showing several input-output pairs where the output is valid JSON teaches the pattern in-context without retraining. This is the most reliable prompt-engineering technique for enforcing structural output constraints.

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Chain-of-thought prompting

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Temperature setting to 0

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Using a larger model variant

Question 9

A hospital deploys an AI system to detect pneumonia from chest X-rays. The model achieves 95% accuracy on the test set but later is found to be less accurate for patients under 18. The development team suspects bias. Which step should be taken first to investigate?

Accepted Answer

Compute and compare performance metrics for different age subgroups in the test set.. The first step in investigating suspected model bias is to perform a disaggregated analysis of performance metrics across relevant subgroups, such as age brackets. This directly identifies whether the model's accuracy, precision, recall, or other metrics differ significantly for pediatric patients versus adults, confirming the presence and nature of the bias before any remediation is attempted.

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Automatically retrain the model with a balanced dataset including more pediatric cases.

Answer

Expand the test set with more pediatric X-rays and re-evaluate overall accuracy.

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Add more features to the model to capture age-related anatomical differences.

Question 10

Which THREE are key principles of trustworthy AI according to the OECD?

Accepted Answer

Robustness. The OECD's Recommendation on Artificial Intelligence defines trustworthy AI around principles including robustness, transparency, and accountability, so options B, C, and E are correct. Robustness (B) is required because AI systems must function reliably, safely, and securely throughout their lifecycle, including resilience to errors and adversarial manipulation. Transparency (C) is a core principle because AI systems should be understandable and disclose meaningful information about their capabilities, limitations, and decision-making so stakeholders can assess them. Accountability (E) is also essential because organizations and individuals deploying or operating AI must remain answerable for the system's outcomes and provide redress where appropriate. Profitability (A) and scalability (D) are business or engineering goals, not OECD trustworthy-AI principles, so they do not belong.

Answer

Profitability

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Scalability

Question 11

A machine learning engineer trains a decision tree to predict customer churn. The tree achieves 99 percent accuracy on the training set but only 68 percent on a held-out test set. The engineer wants to reduce this gap. Which single action is most likely to improve test performance?

Accepted Answer

Limit the tree depth and require a minimum number of samples per leaf. The large train-test gap signals high variance, meaning the unconstrained decision tree memorized training noise. Pre-pruning through maximum depth and minimum samples per leaf limits how finely the tree can split, reducing variance and improving held-out accuracy without discarding the model or leaking test data.

Answer

Train the tree on the test set as well so it learns the held-out distribution

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Increase the maximum depth of the tree so it can fit more training examples

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Add more features derived from the training set to give the tree more signal

Question 12

A financial services firm deploys a credit-scoring model that must produce explanations for adverse action notices. The compliance team requires that each decision be traceable to the exact model version, input features, and the explanation method used at inference time. The data science team currently logs only predictions and timestamps. Which approach best satisfies the traceability requirement?

Accepted Answer

Log the prediction, the model version identifier, a hash of the input feature vector, and the explanation output for every inference request in an immutable audit store.. Traceability for adverse action notices requires linking each decision to the model version, the exact inputs, and the explanation method. Logging those elements together in an immutable store creates an auditable record. Registry tags, training data retention, and generic request logs each capture only part of the picture and cannot reconstruct an individual decision.

Answer

Enable verbose application logging that records request IDs and HTTP status codes for all inference calls.

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Retrain the model monthly and store the training dataset alongside the production model artifact.

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Use a model registry to promote models to production and tag each release with a semantic version.

Question 13

A hospital's AI triage assistant occasionally returns confident but incorrect recommendations when it encounters patient records with missing lab values. The clinical team wants the system to avoid acting on unreliable inputs until a human reviews them. Which operational control best addresses this need?

Accepted Answer

Implement an input validation and confidence threshold gate that routes low-confidence or incomplete records to a human reviewer.. The safest operational response to confident but incorrect outputs on incomplete inputs is a gate that validates inputs and checks confidence, then escalates uncertain cases to a human. This prevents automated action on unreliable data. Temperature changes, retraining, and dashboards do not block individual unsafe recommendations in real time.

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Increase the model's temperature setting to make its outputs more varied and less overconfident.

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Retrain the model on a larger dataset that includes more examples with missing lab values.

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Add a dashboard that displays model accuracy metrics to the clinical staff in real time.

Question 14

A hospital's AI team is training a diagnostic imaging model on chest X-rays. The dataset is small and contains sensitive patient information. The security team wants to ensure that even if the trained model is stolen, individual patients cannot be identified from it. Which technique should the team apply during training to provide a formal, quantifiable privacy guarantee?

Accepted Answer

Differential privacy with a calibrated noise mechanism. Differential privacy is the only technique listed that offers a formal, quantifiable privacy guarantee by mathematically bounding the influence of any single training record. This ensures that even if the model is compromised, individual patients cannot be reliably identified, which is critical for sensitive medical data.

Answer

Data augmentation with synthetic X-ray images

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Homomorphic encryption of the training data

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Federated learning across hospital sites

Question 15

A government agency uses an AI system to prioritize emergency response calls. An auditor finds that the model's decisions cannot be explained to citizens. Which governance mechanism is most appropriate to address this?

Accepted Answer

Implement a right-to-explanation process with model-agnostic explanation tools.. A right-to-explanation process with model-agnostic tools directly provides understandable reasons for automated decisions, which is the governance mechanism the auditor's finding requires. Replacing the model, publishing data, or disclaiming review do not satisfy the need for contestable, explainable decisions.

Answer

Publish the model's full training dataset.

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Replace the model with a simpler linear regression.

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Add a disclaimer that decisions are final and not subject to review.

Question 16

A hospital deploys an AI model to predict patient readmission risk. The compliance team asks which TWO technical controls help comply with data minimization principles under AI governance frameworks. (Choose two.)

Accepted Answer

Use federated learning to keep raw data on local devices.. Differential privacy and federated learning both limit how much individual data influences the model or leaves its source, which is the core of data minimization. Encryption, consent, and simple de-identification do not reduce the scope of data collection or use, so they do not satisfy the principle as directly.

Answer

Anonymize data by removing patient names before training.

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Store all training data in a single encrypted data lake.

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Require users to consent to broad data usage terms.

Question 17

A media company is deploying a generative AI assistant to summarize customer support calls. The assistant must produce concise summaries in English, but the call transcripts are in Spanish. The team wants to use a single model that can handle both translation and summarization. Which approach is MOST appropriate?

Accepted Answer

Use a multilingual large language model and prompt it to translate and summarize in one step.. A multilingual LLM can directly process Spanish transcripts and generate English summaries in one step, satisfying the need for a single model that handles both translation and summarization. This reduces pipeline complexity and avoids error propagation from separate components, making it the most efficient and effective solution for the described scenario.

Answer

Use a Spanish-only language model and prompt it to output English summaries.

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Fine-tune a monolingual English summarization model on translated Spanish transcripts.

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Deploy a dedicated machine translation model first, then pass the English output to a separate summarization model.

Question 18

A financial institution is deploying a real-time anomaly detection model on a Kubernetes cluster. The model must process streaming transactions with low latency and scale horizontally during peak hours. The team wants to use a serving solution that integrates natively with Kubernetes and supports autoscaling based on request concurrency. Which solution best meets these requirements?

Accepted Answer

KServe with Knative autoscaling. KServe with Knative autoscaling provides native Kubernetes integration and request-concurrency-based scaling, which is essential for handling unpredictable transaction volumes with low latency. The other options either lack autoscaling, require manual configuration, or are not optimized for production inference.

Answer

Standalone TensorFlow Serving deployed as a Kubernetes Deployment

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NVIDIA Triton Inference Server with a fixed number of replicas

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A custom Flask API wrapping the model, deployed with a Kubernetes Service

Question 19

A data analyst is exploring a dataset and notices that one numerical feature has a highly skewed distribution with a long right tail. The analyst wants to apply a transformation to make the distribution more symmetric for a linear model. Which transformation is most appropriate?

Accepted Answer

Logarithmic transformation. A logarithmic transformation is the most appropriate for a highly right-skewed distribution because it compresses large values and can make the distribution more symmetric. Square root is milder and less effective for high skewness, while standardization and min-max normalization only rescale without altering distribution shape.

Answer

Standardization (z-score normalization)

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Square root transformation

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Min-max normalization

Question 20

An AI team is optimizing a convolutional neural network (CNN) for inference on a mobile device. The model has many layers and uses 32-bit floating-point weights. They need to reduce the model size and latency without significant accuracy loss. Which technique should they apply?

Accepted Answer

Quantization. Quantization is the most effective technique to reduce model size and latency on mobile devices by converting 32-bit floating-point weights to lower precision, such as 8-bit integers. This leverages hardware acceleration for integer operations and reduces memory bandwidth. Pruning and knowledge distillation can also help but are not as directly targeted at precision reduction.

Answer

Pruning

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Knowledge distillation

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Data augmentation