AI0-001 Practice Test 44 — 15 Questions

Question 1

An AI governance team is implementing the NIST AI Risk Management Framework. They have identified a high-risk AI system and are in the 'Measure' function. Which activity is most appropriate for this function?

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

Accepted Answer

Conduct bias and fairness impact assessments on the model. In the NIST AI Risk Management Framework (AI RMF), the 'Measure' function focuses on assessing and analyzing risks associated with AI systems. For a high-risk AI system, conducting bias and fairness impact assessments is a core activity within this function, as it quantifies and evaluates potential harms related to fairness, accuracy, and transparency. This aligns with the framework's emphasis on quantitative and qualitative risk measurement before moving to risk treatment in the 'Manage' function.

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Implement technical controls to mitigate identified risks

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Document the system's intended purpose and data sources

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Establish an AI ethics board to oversee risk decisions

Question 2

A security analyst is evaluating adversarial threats to a deployed image classifier. Which attack involves making tiny, often imperceptible changes to input images to cause misclassification?

Accepted Answer

Adversarial examples. Adversarial examples are crafted by adding small perturbations to input data that are imperceptible to humans but cause the model to output incorrect predictions.

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Model inversion

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Membership inference

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Data poisoning

Question 3

A company trains a sentiment analysis model on customer reviews. An attacker submits hundreds of reviews with the word 'excellent' attached to negative feedback, causing the model to classify negative reviews as positive. This is an example of which attack?

Accepted Answer

Data poisoning. Data poisoning occurs when an attacker deliberately corrupts the training data to manipulate the model's behavior. By injecting hundreds of reviews that pair the word 'excellent' with negative sentiment, the attacker shifts the model's learned decision boundary, causing it to misclassify genuinely negative reviews as positive. This directly undermines the integrity of the training dataset, which is the hallmark of a data poisoning attack.

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Model extraction

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Adversarial example

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Prompt injection

Question 4

A data scientist is training a neural network to classify images of handwritten digits. The model achieves 99% accuracy on training data but only 85% on validation data. Which technique should the scientist apply first to address this issue?

Accepted Answer

Apply L2 regularization to the network weights. The model shows high training accuracy (99%) but lower validation accuracy (85%), which is a classic sign of overfitting. L2 regularization (option C) adds a penalty term to the loss function proportional to the squared magnitude of the weights, discouraging the network from learning overly complex patterns that do not generalize. This directly addresses overfitting without reducing the model's capacity too aggressively.

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Remove one or more hidden layers from the network

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Increase the number of training epochs

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Add more features to the input data

Question 5

A data science team uses Vertex AI for model training and deployment. They want to implement CI/CD for ML pipelines. Which THREE Google Cloud services should they integrate?

Accepted Answer

Vertex AI Pipelines. Vertex AI Pipelines orchestrates ML workflows; Cloud Build automates builds; Cloud Deploy manages deployments. BigQuery is for analytics; GKE is for containers but not CI/CD specific.

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BigQuery

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Google Kubernetes Engine (GKE)

Question 6

A company is developing an AI-driven recruitment tool. To comply with the EU AI Act's high-risk requirements, which TWO of the following are mandatory obligations?

Accepted Answer

Maintain technical documentation and risk management. High-risk AI systems under the EU AI Act must implement human oversight mechanisms and maintain a technical documentation and risk management system. Transparency disclosure is required, but not necessarily to users in all cases (it depends on context). The other options are not mandatory for all high-risk systems.

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Publish the model's source code publicly

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Conduct a data protection impact assessment (DPIA)

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Disclose to users that they are interacting with an AI system

Question 7

An AI team is evaluating whether to use AI for a customer segmentation task. They have a dataset of customer demographics and purchase history. Which TWO conditions would make AI a better choice than a traditional rule-based approach? (Select two.)

Accepted Answer

The data contains complex, non-linear patterns that are not easily captured by rules. AI techniques like neural networks or gradient-boosted trees excel at capturing complex, non-linear interactions in high-dimensional data (e.g., purchase sequences combined with demographics) that rule-based systems cannot express without an explosion of brittle, hand-crafted conditions. This makes AI the better choice when the underlying patterns are not linearly separable or easily codified as if-then logic.

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The segmentation criteria are well-understood and can be expressed in simple if-then rules

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The segmentation must be fully explainable to regulators

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The team has no access to labeled data

Question 8

A financial institution uses a machine learning model to approve loan applications. The model was trained on historical data that inadvertently encoded a bias against applicants from certain zip codes, leading to discriminatory lending practices. A recent audit reveals that the model's decisions are unfair, and regulators require the bank to remediate the bias without significantly reducing overall approval accuracy. The data science team has access to the training data, the model, and a set of fairness metrics. They also have a small, unbiased validation set. Which course of action should the team take to satisfy regulatory requirements?

Accepted Answer

Implement adversarial debiasing using the unbiased validation set to enforce fairness constraints. Adversarial debiasing directly addresses the bias encoded in the model by training a predictor and an adversary simultaneously. The adversary tries to predict the protected attribute (e.g., zip code) from the model's predictions, while the predictor is penalized for allowing such inference, enforcing fairness constraints. Using the unbiased validation set ensures the debiasing process is guided by ground truth labels that are free from historical bias, allowing the model to retain high accuracy while reducing discrimination.

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Remove the zip code feature from the model and retrain

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Increase the weight of samples from disadvantaged zip codes in the training data

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Retrain the model using only the unbiased validation set

Question 9

An organization wants to implement a robust MLOps pipeline. Which THREE components are essential for a complete MLOps framework? (Choose three.)

Accepted Answer

Continuous integration and continuous deployment (CI/CD) pipeline. A is correct because a CI/CD pipeline automates the integration of code changes and deployment of models into production, ensuring consistent and reliable releases. In MLOps, this includes building, testing, and deploying both application code and ML model artifacts, which is fundamental for operationalizing machine learning at scale.

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Automated code review and approval gates

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Real-time model monitoring dashboard

Question 10

Which TWO techniques are commonly used to handle missing data in a dataset?

Accepted Answer

Remove rows with missing values. Removing rows with missing values is a straightforward technique to handle missing data, especially when the missingness is random and the dataset is large enough that dropping a few rows does not significantly reduce the sample size or introduce bias. Option D is correct because imputing missing values with the mean or median is a common statistical method that preserves the dataset size and is simple to implement, though it can reduce variance and may distort relationships if the data is not missing completely at random.

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Feature scaling

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One-hot encoding

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Principal component analysis (PCA)

Question 11

A data science team deployed a model for real-time predictions. After two weeks, the model's accuracy dropped from 92% to 80%. The monitoring system shows no data drift in features, but the target variable distribution has shifted. Which approach should the team use to detect this issue?

Accepted Answer

Monitor the distribution of the predicted target variable over time. Monitoring the distribution of the predicted target variable directly detects concept drift, which occurs when the relationship between features and the target changes. Since the monitoring system shows no data drift in features, the accuracy drop is likely due to a shift in the target variable's distribution, and tracking predictions over time reveals this shift. This approach aligns with MLOps best practices for detecting concept drift without requiring immediate retraining.

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Schedule manual weekly reviews of model predictions

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Retrain the model immediately with new data

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Monitor input feature distributions using a KS test

Question 12

An AI system used for resume screening is found to consistently reject female candidates for technical roles. The data science team retrains the model after removing the 'gender' feature, but the bias persists. What is the most likely cause?

Accepted Answer

The model uses proxy variables that correlate with gender. Even after removing the explicit 'gender' feature, the model can still learn biased patterns from proxy variables that correlate strongly with gender, such as years of experience (which may be lower for women due to career breaks), educational institutions attended, or even hobbies listed on resumes. These proxies act as surrogates for the protected attribute, allowing the model to effectively discriminate despite the feature being removed. This is a well-known phenomenon in algorithmic fairness called 'redundant encoding' or 'proxy discrimination.'

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The model architecture is too complex

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The training data still contains historical hiring bias

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The evaluation metric does not measure fairness

Question 13

A data scientist is training a neural network to classify images of animals. The training accuracy is 99%, but validation accuracy is only 65%. Which technique should the data scientist use to address this issue?

Accepted Answer

Add dropout layers to the network. The high training accuracy (99%) and low validation accuracy (65%) indicate overfitting, where the model memorizes the training data but fails to generalize. Adding dropout layers randomly drops neurons during training, which forces the network to learn more robust features and reduces overfitting. This technique is specifically designed to improve generalization without requiring more data or altering the learning rate.

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Apply batch normalization

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Increase the number of training epochs

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Increase the learning rate

Question 14

A data science team is developing a churn prediction model. Which TWO data preparation best practices are MOST important to prevent overfitting and ensure generalization?

Accepted Answer

Split data into training and test sets before any preprocessing. Splitting data before any preprocessing prevents leakage from test set into training. Cross-validation provides a more robust estimate of generalization than a single hold-out set.

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Normalize all features using the entire dataset's statistics

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Remove outliers based on the full dataset distribution

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Encode categorical variables with target encoding on the full dataset

Question 15

An AI model for skin cancer detection achieves high accuracy but performs poorly on dark skin tones. The team wants to evaluate whether the model is calibrated across skin tones. Which fairness metric should they use?

Accepted Answer

Calibration. Calibration is the correct metric because it directly measures whether the predicted probabilities of skin cancer match the actual outcomes across different skin tones. A model can have high overall accuracy but be miscalibrated for a subgroup if its confidence scores are systematically over- or under-confident for that group. In this scenario, the team needs to check if the model's risk scores are equally reliable for dark skin tones as for light skin tones, which is exactly what calibration assesses.

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Equalised odds

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Demographic parity

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Individual fairness