AI0-001 Practice Test 41 — 15 Questions

Question 1

A data science team uses Git for version control of model code and DVC for data versioning. They want to implement a model registry to track trained models, their hyperparameters, and performance metrics. Which tool is specifically designed for this purpose and integrates with the existing workflow?

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

Accepted Answer

MLflow Model Registry. MLflow Model Registry is specifically designed for managing model versions, tracking metadata, and integrating with Git and DVC. Apache Airflow is for workflow orchestration, not model registry. Kubernetes is for container orchestration. Docker is for containerization.

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Apache Airflow

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Docker

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Kubernetes

Question 2

Refer to the exhibit. A security auditor identifies a critical vulnerability that could allow an attacker to manipulate model inputs to cause misclassification. Which configuration setting is most directly responsible for this vulnerability?

Accepted Answer

enable_adversarial_defense = false. The vulnerability described is an adversarial attack on model inputs, which directly exploits the absence of adversarial defenses. Setting `enable_adversarial_defense = false` disables mechanisms like adversarial training or input perturbation detection that prevent misclassification from manipulated inputs. This configuration is the most direct root cause because it explicitly turns off the defense designed to counter such attacks.

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enable_input_sanitization = true

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audit_level = basic

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pii_detection = enabled

Question 3

A generative AI model produces images from text prompts. The outputs are often blurry and lack fine details. Which model type is MOST likely being used, and which improvement would best address this issue?

Accepted Answer

Variational Autoencoder (VAE); switch to a diffusion model. Variational Autoencoders (VAEs) are known for producing blurry outputs because their loss function (ELBO) encourages pixel-wise averaging, which smooths out fine details. Diffusion models, by contrast, iteratively denoise a random field, learning to reconstruct high-frequency details through a multi-step reverse process, directly addressing the blurriness issue.

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Variational Autoencoder (VAE); switch to a Generative Adversarial Network (GAN)

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Generative Adversarial Network (GAN); increase the discriminator's capacity

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Diffusion model; use a larger batch size during training

Question 4

An AI team wants to version control datasets, track experiments, and log model parameters across multiple projects. Which MLOps platform is specifically designed for experiment tracking and model management?

Accepted Answer

MLflow. MLflow is an open-source MLOps platform specifically designed for experiment tracking, model management, and reproducibility. It provides a unified API to log parameters, metrics, and artifacts across multiple projects, making it the correct choice for versioning datasets, tracking experiments, and managing models.

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SageMaker Pipelines

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Vertex AI Pipelines

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Kubeflow

Question 5

Which of the following is a key advantage of using ONNX (Open Neural Network Exchange) format for model deployment?

Accepted Answer

It enables framework interoperability for model inference. ONNX provides a standardized, open format for representing machine learning models, enabling seamless interoperability between different frameworks (e.g., PyTorch, TensorFlow, scikit-learn). This allows a model trained in one framework to be deployed for inference using a different runtime or hardware accelerator without requiring retraining or manual conversion, which is a key advantage in heterogeneous production environments.

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It automatically quantizes models to INT8

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It compresses model size by 90%

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It reduces training time

Question 6

A data scientist is using a Hugging Face transformer model for a sentiment analysis task. They want to optimize inference latency for a mobile app. Which model format and framework combination is BEST suited for on-device deployment?

Accepted Answer

Convert to TensorFlow Lite (TFLite) and run on the device. TensorFlow Lite (TFLite) is specifically designed for on-device machine learning inference on mobile and edge devices. It provides a lightweight runtime, hardware acceleration via delegates (e.g., GPU, NNAPI), and reduced model size through quantization, making it the best choice for optimizing inference latency in a mobile app. Converting a Hugging Face transformer model to TFLite allows the model to run locally without network latency, which is critical for real-time sentiment analysis on a smartphone.

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Use the full PyTorch model with JIT scripting

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Deploy the model on a cloud endpoint and call via REST API

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Export to ONNX and use ONNX Runtime with GPU

Question 7

A company is evaluating a vendor's AI system for hiring. The vendor claims the system is fair because it achieves demographic parity. However, the company discovers that the system has significantly different false positive rates across groups. Which fairness issue does this indicate?

Accepted Answer

The system violates equalised odds. Equalised odds requires that a model's false positive rates and true positive rates are equal across all demographic groups. Since the vendor's system has significantly different false positive rates across groups, it violates the equalised odds fairness criterion, even if demographic parity (equal selection rates) is satisfied. This is a core fairness metric in AI governance, as it ensures that errors are distributed equitably.

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The system violates individual fairness

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The system suffers from selection bias

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The system is not calibrated

Question 8

A data scientist is training a supervised learning model for customer churn prediction. Which TWO types of bias are most likely to affect the model's fairness and accuracy if not addressed?

Accepted Answer

Selection bias. Selection bias (B) occurs when the training data does not represent the true customer population, e.g., using only data from a specific time period or region, leading to a model that fails to generalize. Measurement bias (C) arises from systematic errors in how features are recorded, such as inconsistent data collection methods across customer segments, which can skew predictions and harm fairness.

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Algorithmic bias

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Sampling bias

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Confirmation bias

Question 9

Refer to the exhibit. A data scientist is training a binary classifier. Based on the training log, which problem is the model experiencing?

Accepted Answer

Overfitting. The training log shows that the model's training accuracy continues to improve while the validation accuracy plateaus or degrades after a certain number of epochs. This divergence between training and validation performance is the hallmark of overfitting, where the model memorizes the training data noise rather than learning generalizable patterns.

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Underfitting

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Data leakage

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Vanishing gradient

Question 10

A healthcare startup is developing a deep learning model to detect diabetic retinopathy from retinal fundus images. The dataset contains 50,000 images, but only 5% are labeled as positive for the disease. The team uses a convolutional neural network (CNN) with a final sigmoid layer and binary cross-entropy loss. After training for 20 epochs, the model achieves 95% accuracy on the test set, but the recall for the positive class is only 10%. The team suspects the model is biased toward the negative class due to class imbalance. The data is stored in a secure environment, and no additional labeled data can be obtained. The team has access to the following techniques: oversampling the minority class, undersampling the majority class, using class weights in the loss function, applying data augmentation, and using a different architecture. Which course of action is most likely to improve recall for the positive class while maintaining reasonable overall performance?

Accepted Answer

Assign higher class weights to the positive class in the loss function. Assigning higher class weights to the positive class in the loss function directly penalizes misclassifications of the minority class during training. This forces the model to pay more attention to positive samples without altering the dataset distribution, which is critical when no additional labeled data can be obtained and the data is in a secure environment. It improves recall by increasing the gradient contribution from positive samples, while maintaining overall performance because the model still sees the original data distribution.

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Undersample the majority class to balance the dataset

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Oversample the minority class using synthetic image generation

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Replace the CNN with a transformer-based architecture

Question 11

A national security agency uses AI to analyze surveillance data for threat detection. The system is deployed in a high-stakes environment where false negatives could lead to missed threats, and false positives waste analyst time. Recently, a known hacker group attempted to evade detection by subtly modifying their communication patterns over time, a form of adversarial evasion. The agency wants to harden the system while maintaining performance. The system uses a deep neural network. Which mitigation strategy is most appropriate?

Accepted Answer

Perform adversarial training using the hacker group's known evasion patterns. Adversarial training is the most appropriate mitigation because it directly incorporates known evasion patterns into the training process, making the deep neural network robust to the hacker group's subtle modifications. By retraining the model on adversarial examples, the decision boundary is hardened against these specific attacks without sacrificing overall detection performance. This approach is a standard defense in high-stakes security AI, balancing false positive and false negative rates while countering adversarial evasion.

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Switch to an unsupervised learning approach to detect anomalies

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Simplify the model to a logistic regression to reduce the attack surface

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Add random noise to all input data to confuse evasion attempts

Question 12

A team is implementing a RAG system for a legal document Q&A. They need to chunk documents effectively. Which THREE chunking strategies should they consider to improve retrieval accuracy for legal texts that contain hierarchical sections (clauses, sub-clauses, definitions)?

Accepted Answer

Hierarchical chunking that indexes chunks at clause and sub-clause levels with parent relationships. Semantic chunking splits at natural boundaries (e.g., paragraphs, sections), preserving meaning. Hierarchical chunking indexes with parent-child relationships for context. Fixed-size chunking is simple but may break sentences or clauses. Overlapping chunks can help but is not a primary strategy for accuracy; sliding window is a specific technique.

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Overlapping chunks with a 10% overlap between consecutive chunks

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Fixed-size chunking with a 512-token window and no overlap

Question 13

A dataset used for training a classification model contains 10% missing values in a feature that is known to be important. The data scientist decides to impute the missing values. Which imputation method is most robust if the data is not missing completely at random?

Accepted Answer

Use multiple imputation to model missing values. Multiple imputation is the most robust method when data is not missing completely at random (NMAR) because it uses a statistical model to account for the relationships between the missing feature and other observed features, generating multiple plausible values and combining them to produce unbiased estimates and valid standard errors. This approach preserves the variability and structure of the data, unlike simpler methods that can introduce bias when missingness depends on unobserved data.

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Delete all rows with missing values

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Replace missing values with the mean of the feature

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Fill missing values with 0

Question 14

Under the EU AI Act, an AI system that uses subliminal techniques to materially distort a person's behaviour, causing psychological or physical harm, would be classified under which risk tier?

Accepted Answer

Unacceptable risk. The EU AI Act categorises such systems as 'unacceptable risk' and prohibits them outright. High risk includes critical infrastructure, education, employment, etc. Limited risk involves transparency obligations. Minimal risk covers all other systems.

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Limited risk

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High risk

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Minimal risk

Question 15

You are an AI governance officer at a bank that uses a machine learning model to predict credit risk. The model was developed by an external vendor and uses a proprietary algorithm. The bank's compliance team has determined that the model must be explainable to meet regulatory requirements. However, the vendor claims the model is a 'black box' and cannot provide explanations. You need to ensure compliance while maintaining the model's performance. What is the best course of action?

Accepted Answer

Use a model-agnostic explanation technique like SHAP. D is correct because model-agnostic explanation techniques like SHAP (SHapley Additive exPlanations) can provide post-hoc interpretability for any black-box model without requiring access to its internal structure or proprietary algorithm. This allows the bank to meet regulatory explainability requirements while preserving the vendor's proprietary model and its predictive performance.

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Ignore the requirement as the model is proprietary

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Ask the vendor to develop a custom explanation module

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Replace the model with a simpler, interpretable model