AI0-001 Practice Exam 52 — 20 Questions

Question 1

Which TWO are key requirements for AI governance under the EU AI Act for high-risk AI systems? (Choose two.)

Accepted Answer

Human oversight. Option B (Human oversight) is correct because the EU AI Act explicitly requires that high-risk AI systems be designed and developed with appropriate human oversight mechanisms, allowing humans to effectively monitor, intervene, and override the system to prevent or minimize risks to health, safety, and fundamental rights. Option D (Transparency and documentation) is correct because high-risk AI systems must be accompanied by technical documentation, instructions for use, and logging capabilities that ensure traceability and enable users and authorities to understand the system's functioning and compliance. Options A, C, and E are not key requirements under the Act: regular performance benchmarks are not a mandated governance requirement per se, open-source licensing is not a condition for high-risk compliance (though open-source models have some accommodations), and there is no mandatory use of cloud infrastructure for high-risk AI systems.

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Regular performance benchmarks

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Open-source licensing

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Mandatory use of cloud

Question 2

A data scientist is selecting a model for a binary classification task where interpretability is critical because of regulatory requirements. The dataset has 20 features and 10,000 samples. Which model is MOST appropriate?

Accepted Answer

Decision tree. A decision tree is inherently interpretable: its if-then-else splits can be visualized and explained to regulators, auditors, or customers. With only 20 features and 10,000 samples, a single tree is also computationally adequate and unlikely to overfit catastrophically if pruned. This makes it the best fit when interpretability is a hard requirement.

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Neural network (MLP)

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Gradient boosting machine

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Random forest classifier

Question 3

An AIOps platform monitors server metrics and triggers alerts. The team notices too many false positives. Which adjustment should be made to the anomaly detection model?

Accepted Answer

Raise the anomaly score threshold for triggering alerts.. Raising the anomaly score threshold (Option D) directly reduces false positives by requiring a higher deviation from normal behavior before an alert is triggered. In AIOps platforms, the anomaly score is a numeric value (e.g., 0–100) that quantifies how unusual a metric is; a higher threshold means only more extreme deviations generate alerts, filtering out minor fluctuations that were incorrectly flagged.

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Use a more complex model to better fit the data.

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Shorten the observation window to detect anomalies faster.

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Increase the training data to include more normal patterns.

Question 4

An AI system used for hiring has been found to exhibit racial bias against certain candidates. Which step should the organization take to mitigate this?

Accepted Answer

Regularly audit model predictions across demographic groups and retrain with fairness constraints.. Bias in AI systems is often embedded in training data or model behavior, not just in feature selection. Regularly auditing predictions across demographic groups and retraining with fairness constraints (e.g., demographic parity or equalized odds) allows the organization to detect and correct disparate impact without sacrificing model performance. This aligns with the AI0-001 focus on continuous monitoring and iterative improvement in AI operations.

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Remove all demographic features from the model.

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Use a different algorithm that is inherently unbiased.

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Hire more diverse data scientists.

Question 5

A self-driving car company is developing an object detection system using a convolutional neural network (CNN). The system needs to detect pedestrians and vehicles in real-time with high accuracy. Which technique can reduce inference time while maintaining accuracy?

Accepted Answer

Apply model pruning and quantization. Model pruning removes redundant or less important weights from the CNN, reducing computational load, while quantization converts floating-point weights to lower-precision integers (e.g., INT8). Together, they shrink model size and speed up inference without significantly degrading accuracy, making them ideal for real-time object detection in resource-constrained environments like autonomous vehicles.

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Use a pre-trained model and fine-tune it

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Add more convolutional layers

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Increase number of filters in each layer

Question 6

A large e-commerce company uses a recommendation system based on collaborative filtering. The system uses a matrix factorization model that is trained nightly on the entire user-item interaction history. Recently, the company launched a flash sale with thousands of new products. Users are reporting that the recommendations are not showing the new products, even for users who have purchased them during the sale. The data engineering team notices that the new products have very few interactions in the training data. The model's loss on the validation set has increased, and the recall@10 metric has dropped from 0.45 to 0.32. The team needs to improve the recommendation of new items without retraining the entire model from scratch every hour. Which approach should the team take?

Accepted Answer

Use a hybrid model that combines collaborative filtering with content-based features from product metadata. A hybrid model that combines collaborative filtering with content-based features (e.g., product metadata like category, price, or description) can recommend new products even with zero or very few user interactions. The content-based component leverages item attributes to compute similarity between new and existing items, enabling the system to surface new products without requiring extensive interaction history. This approach addresses the cold-start problem for new items while preserving the collaborative filtering signal for established items, and it does not require retraining the entire model from scratch every hour.

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Retrain the model every hour to incorporate new interactions quickly

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Remove the new products from the recommendation pool until they accumulate enough interactions

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Increase the number of latent factors in the matrix factorization model

Question 7

An AI model is trained to predict loan default. The training data contains 95% non-default and 5% default. Which metric is most appropriate to evaluate model performance given the imbalanced dataset?

Accepted Answer

F1-score. The F1-score is the harmonic mean of precision and recall, making it robust to class imbalance. In this dataset with 95% non-default and 5% default, accuracy would be misleadingly high (95%) even if the model never predicts default, while F1-score penalizes poor recall of the minority class.

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Mean squared error

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Accuracy

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R-squared

Question 8

An AI team notices that their model's performance degrades over time because the statistical relationship between input features and the target variable changes. This issue is called:

Accepted Answer

Concept drift. Concept drift occurs when the statistical relationship between input features and the target variable changes over time, causing model performance to degrade. This is distinct from data drift, which involves changes in the input data distribution alone. In the AI0-001 context, concept drift directly addresses the shift in the underlying mapping from features to labels.

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Data drift

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Overfitting

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Model drift

Question 9

A data scientist notices the model overfits. Which change to the exhibit's configuration would most likely reduce overfitting?

Accepted Answer

Add L2 regularization to dense layers. Adding L2 regularization to dense layers penalizes large weights by adding a squared magnitude term to the loss function, which forces the model to learn simpler patterns and reduces overfitting. This directly addresses the core issue of the model memorizing noise in the training data.

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Remove dropout layers

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Increase learning rate to 0.01

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Increase units in the first dense layer to 512

Question 10

A team is building a natural language processing (NLP) model to analyze customer feedback. They have a large corpus of unlabeled text data and want to generate word embeddings that capture semantic meaning. Which approach should they use?

Accepted Answer

Word2Vec. Word2Vec is the correct approach because it learns dense, distributed word embeddings from large unlabeled corpora by training a shallow neural network to predict words in context (CBOW) or context from words (Skip-gram). This captures semantic relationships such as analogy and similarity, which is essential for analyzing customer feedback without labeled data.

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One-hot encoding

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TF-IDF vectorization

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Bag-of-words model

Question 11

A financial institution needs to integrate an AI-based credit scoring model into an existing mainframe system that processes transactions in COBOL. The model is deployed as a REST API. What is the best strategy to ensure minimal disruption and maintain data integrity?

Accepted Answer

Use an API gateway with versioning and circuit breaker patterns.. An API gateway with versioning and circuit breaker patterns allows the COBOL mainframe to call the REST API without modifying its core transaction logic. The gateway handles protocol translation, rate limiting, and failover, ensuring minimal disruption to the existing mainframe system while maintaining data integrity through transactional consistency and graceful degradation.

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Copy all transaction data to a cloud database for the model to access.

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Rewrite the mainframe system in Java to directly call the model.

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Install a GPU on the mainframe to run the model natively.

Question 12

A company deploys a deep learning model for real-time image classification. After deployment, they notice high inference latency exceeding the 100ms SLA. Which action would most likely reduce latency without significantly impacting accuracy?

Accepted Answer

Apply model quantization. Model quantization reduces the precision of the model's weights and activations (e.g., from 32-bit floating point to 8-bit integer), which significantly decreases memory bandwidth and computational requirements during inference. This directly lowers latency without fundamentally altering the model's learned representations, so accuracy degradation is typically minimal (often <1-2%).

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Add more training data to improve model robustness

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Replace the model with a simpler logistic regression model

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Increase batch size for inference

Question 13

A company has developed a deep learning model for image classification. The team wants to deploy the model to production with high availability and scalability. Which approach should they use?

Accepted Answer

Use a containerized approach with Kubernetes.. Containerization with Kubernetes provides the orchestration, auto-scaling, and self-healing capabilities required for high availability and scalability in production. Kubernetes manages container lifecycles, distributes traffic across replicas via Services and Ingress controllers, and can automatically scale pods based on CPU/memory metrics or custom metrics, ensuring the deep learning model handles variable loads without downtime.

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Run the model on a laptop during business hours.

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Deploy the model as a monolithic application on a single server.

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Embed the model directly into a mobile app.

Question 14

A company uses an AI model to predict equipment failures. The model outputs a probability of failure. To minimize false alarms, the operations team wants a high precision. Which deployment strategy should they implement?

Accepted Answer

Increase the decision threshold for positive classification. To minimize false alarms and achieve high precision, the operations team should increase the decision threshold for positive classification. A higher threshold means the model only predicts a failure when it is very confident, reducing the number of false positives (false alarms) at the cost of potentially missing some true failures (lower recall). This directly controls the precision-recall trade-off without changing the underlying model.

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Retrain the model on more recent data

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Decrease the decision threshold

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Use an ensemble of models with voting

Question 15

A financial institution uses a machine learning model to approve personal loans. The model was trained on historical data that includes applicant age, income, credit score, and loan amount. Compliance officers have received customer complaints suggesting the model may be discriminating against applicants over 60 years old. Initial analysis shows that the approval rate for applicants over 60 is 20 percentage points lower than for younger applicants with similar credit profiles. The data science team has been asked to investigate and remediate any bias. They have access to the training data, model coefficients, and can retrain or modify the model. What is the FIRST step the team should take?

Accepted Answer

Conduct a fairness audit using appropriate metrics such as disparate impact ratio on the current model.. The first step in addressing potential bias is to conduct a fairness audit using established metrics like the disparate impact ratio (e.g., the 80% rule from the US Equal Employment Opportunity Commission). This quantifies whether the model's approval rate for applicants over 60 is less than 80% of the rate for the younger group, providing a legally and technically sound baseline before any remediation. Without this measurement, any subsequent changes (like resampling or removing features) could be misguided or ineffective.

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Replace the model with a third-party vendor model that claims to be bias-free.

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Re-sample the training data to have equal numbers of applicants over and under 60.

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Remove the age feature from the training data and retrain the model.

Question 16

Under the GDPR, individuals have the right to not be subject to a decision based solely on automated processing if it produces legal effects. Which of the following is a typical safeguard that organisations must provide to comply with this right?

Accepted Answer

The right to obtain human intervention on the part of the controller. GDPR Article 22(3) explicitly provides that where solely automated decision-making (including profiling) produces legal or similarly significant effects, the data subject has the right to obtain human intervention, express their point of view, and contest the decision. Human intervention is the canonical safeguard organisations must offer.

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The right to have all personal data deleted immediately

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The right to receive a detailed mathematical explanation of the model

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The right to demand a more favourable automated decision

Question 17

A data scientist is building a model to predict whether a credit card transaction is fraudulent, using labeled historical data. Which machine learning paradigm is being used?

Accepted Answer

Supervised learning. Supervised learning uses labeled historical data to train a model that predicts a target variable. Here, the credit card transactions are labeled as fraudulent or not, so the model learns from these labels to classify new transactions.

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Reinforcement learning

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Unsupervised learning

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Self-supervised learning

Question 18

Which principle ensures that AI decisions can be traced back and understood by humans?

Accepted Answer

Transparency. Transparency is the principle that ensures AI decisions can be traced back and understood by humans. It requires that the internal workings of an AI model, including its inputs, decision paths, and outputs, are documented and interpretable, enabling auditability and trust. Without transparency, stakeholders cannot verify whether the AI system is behaving as intended or complying with ethical and regulatory standards.

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Privacy

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Robustness

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Accountability

Question 19

A data scientist is preparing a dataset for a binary classification model. The dataset has 1000 samples, with 800 positives and 200 negatives. To evaluate the model properly, which THREE steps should they take? (Select THREE)

Accepted Answer

Use a stratified train-test split to preserve class proportions. Option B is correct because a stratified train-test split preserves the 80/20 class ratio (800 positives, 200 negatives) in both the training and test sets, ensuring the evaluation reflects the true class distribution and avoids sampling bias. Option C is correct because SMOTE generates synthetic minority-class samples by interpolating between existing minority instances, balancing the training set so the classifier does not become biased toward the majority class. Option E is correct because with imbalanced data, accuracy is misleading; precision, recall, and F1-score reveal how well the model identifies the minority class and balances false positives against false negatives. Option A is wrong because deleting minority samples discards valuable information and worsens the class imbalance problem. Option D is wrong because a model predicting all positives would achieve 80% accuracy while completely failing on the minority class, making accuracy an unreliable metric here.

Answer

Remove the minority class samples to make the dataset balanced

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Report only accuracy as the evaluation metric

Question 20

A developer is deploying an AI service API. To protect against data leakage through API responses, which access control principle should be applied to API keys?

Accepted Answer

Implement least-privilege API access with scoped permissions. The least-privilege principle ensures that each API key is scoped to only the specific permissions required for its intended function, such as read-only access to a single endpoint. This minimizes the blast radius in case the key is compromised, preventing unauthorized access to other services or data. In AI service deployments, scoped permissions are often enforced via OAuth 2.0 scopes or IAM roles tied to the API key.

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Disable API keys and rely on IP whitelisting only

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Use a single shared API key for all services

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Grant all API keys full access to simplify management