AI0-001 Practice Test 5 — 25 Questions

Question 1

In the AI project lifecycle, which phase involves partitioning the dataset into training, validation, and test sets?

Accepted Answer

Data preparation. Data preparation includes splitting the data to evaluate model performance and prevent leakage.

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Data acquisition

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Model selection

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Problem definition

Question 2

A company uses AI to generate marketing images. They want to ensure that the images are clearly identified as AI-generated to comply with transparency obligations. Which approach is most effective?

Accepted Answer

Embed a visible watermark stating 'AI-generated' in each image. A visible watermark embedded directly in each generated image is the most effective transparency measure because it travels with the image regardless of where it is copied, screenshotted, or re-shared, and it is immediately perceivable by any viewer. Metadata can be stripped, and terms of service are not seen by end viewers, so a visible watermark is the only option that guarantees the AI-generated nature is disclosed at the point of consumption.

Answer

Add a disclaimer in the platform's terms of service

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Include metadata in the image file indicating it is AI-generated

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Rely on deepfake detection algorithms to flag the images

Question 3

A government agency is deploying an AI model to screen loan applications. The model uses features like income, credit score, employment history, and zip code. During fairness auditing, the model is found to deny a disproportionately high number of applicants from a particular demographic group, even when controlling for legitimate financial factors. The agency wants to mitigate this bias without significantly reducing overall accuracy. Which approach should the data scientist prioritize?

Accepted Answer

Use adversarial debiasing during model training. Adversarial debiasing is the correct approach because it directly optimizes the model to remove sensitive information (e.g., demographic group membership) from its internal representations while preserving predictive accuracy. This technique trains a primary model to predict the target (loan approval) and an adversary to predict the protected attribute from the model's learned features, forcing the primary model to learn representations that are both accurate and unbiased. It addresses the root cause of bias—correlation between protected attributes and model predictions—without requiring post-hoc threshold adjustments or sacrificing overall performance.

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Adjust the decision threshold for the affected group

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Remove the zip code feature from the model

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Apply sample weighting to balance the demographic groups

Question 4

A startup wants to add an AI-powered virtual assistant to their mobile app. They have limited in-house AI expertise and need a solution that can be integrated quickly with minimal infrastructure management. Which deployment pattern is MOST suitable?

Accepted Answer

Use a cloud-based AI microservice (e.g., Amazon Lex, Azure Bot Service) with a pre-built model. Using a cloud-based AI microservice with a pre-built model is most suitable because it requires minimal AI expertise and infrastructure management. The startup can integrate the service via APIs quickly, leveraging the provider's pre-trained models and scalability. This aligns with their need for rapid integration and limited in-house AI skills.

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Implement an asynchronous processing queue for all user requests

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Train and deploy a custom model on an on-premises server

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Deploy the model on edge devices for offline inference

Question 5

A startup wants to identify unusual patterns in network traffic to detect potential security breaches. They have a large dataset of normal traffic but very few labeled attacks. Which machine learning approach is MOST suitable?

Accepted Answer

Unsupervised anomaly detection. Unsupervised anomaly detection is the most suitable approach because the startup has a large dataset of normal traffic but very few labeled attacks. This technique learns the baseline of normal behavior from unlabeled data and flags deviations as potential anomalies, which is ideal for detecting unknown or rare attack patterns without requiring labeled attack samples.

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Supervised classification with logistic regression

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Reinforcement learning

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Semi-supervised learning

Question 6

A hospital's radiology department uses an AI model to detect lung nodules in CT scans. The model was trained on data from a specific brand of scanners and patient demographics common in Europe. Recently, the hospital acquired new scanners from a different manufacturer and started serving a more diverse patient population. Over the past month, the model's false-positive rate has increased by 15% and false-negative rate by 8%. The radiologists are losing confidence and are considering abandoning the AI tool altogether. The IT team has verified that the model inference is running correctly and the hardware is performing as expected. The data science team suspects the problem is related to the change in input data distribution. The hospital's AI operations policy requires that any model update must be validated on at least 500 recent cases before deployment. What is the BEST course of action for the AI operations team?

Accepted Answer

Collect 500 recent CT scans from the new scanners, retrain the model on a combined old and new dataset, and validate before deployment.. The model's performance degradation is likely due to data drift: the input data distribution has changed because of new scanners and a more diverse patient population. The best course is to collect recent data representative of the new distribution, retrain the model on a combined dataset (old and new), and validate on at least 500 recent cases as per policy. This addresses the root cause and ensures the model generalizes to the new data.

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Roll back to the previous model version and restrict use of the AI tool to only European patients.

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Retrain the model using the original training data but with increased regularization to avoid overfitting.

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Adjust the model's decision threshold to reduce false positives and then monitor for two weeks.

Question 7

Refer to the exhibit. What is the recall of the model?

Accepted Answer

0.44. Recall is calculated as True Positives divided by (True Positives + False Negatives). From the confusion matrix in the exhibit, True Positives = 400 and False Negatives = 500, so recall = 400 / (400 + 500) = 400 / 900 ≈ 0.44. Option A is correct because this matches the computed recall value.

Answer

0.80

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0.90

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0.99

Question 8

A hospital wants to train a diagnostic model using data from multiple hospitals without sharing raw patient data. Which technique allows model training across decentralised data while preserving privacy?

Accepted Answer

Federated learning. Federated learning trains a shared model across decentralized data sources by sending model updates (gradients or weights) rather than raw data to a central server, which aggregates them into a global model. This allows multiple hospitals to collaborate on a diagnostic model without ever sharing patient records.

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Differential privacy applied to the combined dataset

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Centralising all data in one location and anonymising it

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Using pseudonymisation and then pooling the data

Question 9

A financial institution is training a risk assessment model. The dataset includes customer credit scores, income, age, and past loan defaults. During feature engineering, a data engineer creates a new feature 'income_to_debt_ratio'. Which type of feature engineering technique is this?

Accepted Answer

Feature combination. 'income_to_debt_ratio' is created by combining two existing features (income and debt) into a single derived feature. This is a classic example of feature combination (also known as feature crossing or feature construction), where arithmetic operations or logical rules are applied to existing variables to generate new predictive signals. The goal is to capture interactions or relationships that the original features alone may not express linearly.

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Feature encoding

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Feature scaling

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Feature selection

Question 10

A company is adopting a secure development lifecycle for its new AI product. Which THREE activities are essential for secure AI development? (Select three.)

Accepted Answer

Implementing secure data pipelines. Option A (Implementing secure data pipelines) is correct because AI systems depend on large volumes of training and inference data, so protecting data in transit and at rest, validating inputs, and preventing poisoning or leakage require hardened, access-controlled pipelines. Option B (Threat modeling using STRIDE) is correct because STRIDE systematically identifies spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege threats across the AI lifecycle, including model theft, adversarial inputs, and prompt injection. Option D (Audit logging of AI interactions) is correct because immutable logs of prompts, model outputs, data access, and administrative actions provide traceability, support incident response, and satisfy governance and compliance requirements for AI systems. Option C is not essential because deploying on the fastest hardware is a performance optimization, not a security control, and may even increase attack surface or cost without improving security. Option E is not essential because homomorphic encryption for all data at rest is impractical for most AI workloads due to severe performance overhead, and it is not a baseline requirement when standard encryption at rest and in transit already addresses the threat.

Answer

Deploying the model on the fastest hardware available

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Using homomorphic encryption for all data at rest

Question 11

A company is deploying an LLM-based system that can execute API calls on behalf of users. Which TWO measures should they implement to prevent excessive agency?

Accepted Answer

Restrict the LLM to read-only or low-risk actions. Option B is correct because limiting the LLM's permissions to read-only or low-risk API operations directly constrains the scope of actions the model can autonomously perform, which is the core defense against excessive agency (least-privilege enforcement). Option D is correct because requiring human-in-the-loop approval for high-risk actions ensures that consequential API calls cannot be executed solely on the model's initiative, adding a human authorization gate before damage can occur. Options A and E address prompt injection and unsafe content at the input/output layer, but they do not limit what actions the agent is authorized to take, so they do not mitigate excessive agency. Option C, rate limiting, only throttles the volume or frequency of API calls; it does not prevent a single unauthorized or high-impact action from being executed, so it is not a primary control for excessive agency.

Answer

Implement strict output filtering

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Apply rate limiting to API calls

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Use input validation to sanitize user prompts

Question 12

A team is developing a threat model for an AI system that processes user uploads. Using STRIDE, which threat involves an attacker modifying the model's training data to cause misclassification?

Accepted Answer

Tampering. Tampering is the STRIDE category for unauthorized modification of data. Data poisoning is a form of tampering with training data.

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Spoofing

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Repudiation

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Information disclosure

Question 13

An AI system is designed to automatically execute actions on behalf of users, such as sending emails. The security team is concerned about excessive agency. Which mitigation is most effective?

Accepted Answer

Restrict the functions the model can call and require human approval for sensitive actions. Excessive agency is mitigated by least-privilege scoping of the tools/functions the model can invoke and inserting human-in-the-loop approval for high-impact actions like sending emails. Restricting callable functions and requiring approval directly limits the blast radius of a compromised or misaligned agent.

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Disable output filtering

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Increase the model's context window

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Use a larger model

Question 14

An organization deploys a machine learning model for credit scoring. An attacker submits carefully crafted loan applications that are slightly outside normal ranges but cause the model to approve high-risk loans. What type of attack is this?

Accepted Answer

Adversarial example. This is an adversarial example attack, where the attacker crafts inputs with small, carefully chosen perturbations that cause the ML model to misclassify them. In credit scoring, submitting loan applications with values slightly outside normal ranges exploits the model's decision boundary to approve high-risk loans, a classic evasion technique.

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Model extraction

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Prompt injection

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Data poisoning

Question 15

An AI system trained on historical medical records shows that certain racial groups have higher predicted risk for a disease. The data reflects real-world differences in diagnosis rates due to unequal access to healthcare. Which type of bias is this?

Accepted Answer

Historical bias. Historical bias occurs when training data reflects pre-existing societal inequalities or biases, even if the data is accurately collected and representative. Here, the medical records show real-world differences in diagnosis rates due to unequal healthcare access, meaning the data itself encodes a historical inequity. The model learns and perpetuates this pattern, predicting higher risk for certain racial groups based on biased historical outcomes rather than true biological differences. Thus, the bias is inherent in the data's origin, not in the algorithm or sampling method.

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Algorithmic bias

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Selection bias

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Confirmation bias

Question 16

A hospital wants to train a diagnostic model using patient data from multiple hospitals without sharing raw patient records. Which technique enables collaborative model training while keeping data decentralised?

Accepted Answer

Federated learning. Federated learning enables multiple parties to collaboratively train a shared model without exchanging raw data. Each hospital trains a local model on its own patient records, and only model updates (e.g., gradients or weights) are sent to a central server for aggregation. This keeps sensitive data decentralised and reduces privacy risks, making it the correct choice for collaborative training across hospitals.

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Pseudonymisation

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Differential privacy

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Anonymisation

Question 17

A company implements a chatbot using a rule-based system. Users complain the chatbot cannot handle new queries. Which AI approach should be considered to improve flexibility?

Accepted Answer

Machine learning. Machine learning (ML) enables a chatbot to learn from new data and adapt to unseen queries, unlike a static rule-based system. By training on historical conversations, an ML model can generalize patterns and handle novel inputs without requiring explicit rules for every scenario.

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Expert system

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Natural language processing (NLP)

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Robotic process automation

Question 18

A team is using a cloud AI service with a pay-per-token pricing model. They want to minimize costs while maintaining response quality. Which strategy is MOST effective?

Accepted Answer

Use prompt caching for repeated query patterns. Prompt caching reduces costs by avoiding redundant token processing for repeated query patterns. The cloud AI service charges per token, so caching the prefix of frequent requests (e.g., system prompts or common context) means only the new, unique tokens are billed, directly lowering expenditure without sacrificing response quality.

Answer

Switch to a smaller, less capable model

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Increase the batch size for API calls

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Reduce the model's max_tokens to a very low value

Question 19

A data scientist needs to explain why a specific loan application was rejected by a tree-based model. The model is complex and not inherently interpretable. Which method should the data scientist use to provide a local explanation for this single prediction?

Accepted Answer

LIME. LIME (Local Interpretable Model-agnostic Explanations) is the correct choice because it is specifically designed to provide local explanations for individual predictions by approximating the complex model with a simpler, interpretable surrogate model around that specific instance. For a tree-based model that is not inherently interpretable, LIME can explain why a single loan application was rejected by perturbing the input and observing the changes in predictions, making it ideal for this use case.

Answer

SHAP values

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Model cards

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Attention visualization

Question 20

Refer to the exhibit. A team deploys a sentiment analysis model with this policy. After one month, the monitoring system triggers an alert for feature drift. Which action should the team take first?

Accepted Answer

Compare the current feature distributions with the training set to identify which features drifted.. When a monitoring system triggers an alert for feature drift, the first step is to diagnose which features have changed. Comparing current feature distributions with the training set identifies the specific features that drifted, enabling targeted remediation such as retraining with recent data or feature engineering. This aligns with the standard MLOps workflow for drift detection and response.

Answer

Review the fairness check settings to ensure protected attributes are still relevant.

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Immediately retrain the model on recent data to adapt to the drift.

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Reduce the classification threshold to 0.5 to increase sensitivity.