AI0-001 Practice Exam 51 — 20 Questions

Question 1

Which TWO of the following are techniques used for reducing overfitting in neural networks? (Choose two.)

Accepted Answer

Dropout. Dropout (A) is correct because it randomly deactivates a fraction of neurons during each training iteration, which prevents the network from relying on specific neurons and forces it to learn more robust, generalizable features, thereby reducing overfitting. L2 regularization (C) is correct because it adds a penalty term proportional to the squared magnitude of the weights to the loss function, discouraging large weights and constraining model complexity to improve generalization. Boosting (B) is an ensemble meta-algorithm that combines weak learners to reduce bias, not a neural-network overfitting-reduction technique. Increasing the learning rate (D) typically causes unstable training or divergence rather than reducing overfitting. Increasing the number of hidden layers (E) raises model capacity and usually worsens overfitting rather than mitigating it.

Answer

Boosting

Answer

Increasing the learning rate

Answer

Increasing the number of hidden layers

Question 2

A company wants to use AI to automatically categorize customer support tickets into topics like 'billing', 'technical', 'account'. They have 10,000 labeled examples. Which algorithm is most suitable for this task?

Accepted Answer

Logistic regression. Logistic regression is a supervised learning algorithm that models the probability of a categorical outcome based on input features. With 10,000 labeled examples, it can efficiently learn decision boundaries to classify tickets into 'billing', 'technical', or 'account' by using a softmax (multinomial logistic regression) extension for multi-class classification.

Answer

DBSCAN

Answer

Apriori

Answer

Principal component analysis (PCA)

Question 3

The exhibit shows a model configuration for a classification task with 10 classes. What is wrong with this setup?

Accepted Answer

The loss function should be categorical crossentropy, not mean squared error. In a multi-class classification task with 10 classes, the correct loss function is categorical crossentropy because it measures the dissimilarity between the true probability distribution and the predicted probability distribution. Mean squared error (MSE) is designed for regression tasks and penalizes errors in a way that is not suitable for classification probabilities, leading to poor gradient behavior and slower convergence.

Answer

The metric should be precision, not accuracy

Answer

The activation should be sigmoid in hidden layers

Answer

The optimizer should be SGD, not Adam

Question 4

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions based on the latest policy documents without retraining the model. By indexing the documents in a vector store and retrieving relevant chunks at query time, RAG provides up-to-date, contextually accurate answers while keeping the underlying LLM static, which avoids the cost and complexity of monthly retraining.

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Fine-tune a base LLM on the policy documents monthly

Answer

Train a custom model from scratch on the policy documents each month

Question 5

An AI system is being implemented in a healthcare setting. Which TWO ethical considerations should be prioritized?

Accepted Answer

Ensuring the model does not exhibit racial or gender bias. Option A is correct because in a healthcare AI system, ensuring the model does not exhibit racial or gender bias is a core ethical requirement: biased training data or features can produce discriminatory diagnostic or treatment recommendations that harm protected patient groups, violating fairness and equity principles in clinical care. Option C is correct because providing explainable predictions to doctors supports transparency, accountability, and informed clinical decision-making; clinicians must understand the basis of AI recommendations to validate them, obtain patient consent, and meet medical-legal and regulatory obligations. Option B is not an ethical consideration but a financial/business objective, and cost reduction does not justify compromising patient welfare. Option D is not appropriate because replacing human judgment entirely with AI removes clinician oversight and accountability, which is ethically and legally unacceptable in healthcare. Option E is also a cost/licensing concern rather than an ethical priority, and using open-source models does not by itself address fairness, transparency, or patient safety.

Answer

Maximizing cost reduction for the hospital

Answer

Replacing human judgment entirely with AI

Answer

Using open-source models to reduce licensing costs

Question 6

A company is conducting a vendor AI assessment for a third-party natural language processing service. They need to ensure the vendor's AI governance practices align with their own. Which THREE areas should they evaluate?

Accepted Answer

The vendor's data handling and privacy practices. Data handling and privacy practices are a core component of AI governance, ensuring that the vendor's use of customer data complies with regulations like GDPR or CCPA and aligns with the company's own data protection policies. During a vendor AI assessment, evaluating how the vendor collects, stores, processes, and secures data is critical to mitigate risks of data breaches, unauthorized use, or non-compliance, which directly impacts trust and legal liability.

Answer

The vendor's model architecture and training framework

Answer

The vendor's marketing budget for AI services

Question 7

An organization uses an LLM to generate financial reports. They want to ensure the model does not output sensitive customer data that it may have memorized during training. Which technique should be implemented in the AI pipeline to detect and block such outputs?

Accepted Answer

Output filtering. Output filtering is the correct technique because it operates after the LLM generates a response, scanning the output for sensitive data patterns (e.g., PII, financial account numbers) and blocking or redacting them before delivery. This directly addresses the risk of the model regurgitating memorized customer data from its training set, which input validation cannot catch since the sensitive data appears only in the output.

Answer

Input validation

Answer

Rate limiting

Answer

Federated learning

Question 8

Which TWO of the following are best practices for monitoring AI models in production?

Accepted Answer

Set up alerts for prediction latency and error rates.. Option A is correct because production monitoring must include operational health metrics such as prediction latency and error rates, which surface service degradation, timeouts, and failed inferences before they impact users. Option E is correct because tracking input data distribution and comparing it against the training data detects data drift and covariate shift, which are leading indicators that model accuracy will degrade even when infrastructure metrics look healthy. Together, A and E cover both system-level reliability and statistical/data-quality monitoring, which are the two core pillars of effective ML observability. Option B is not a best practice because accuracy measured only at deployment time cannot reveal post-deployment degradation caused by drift or changing real-world conditions. Option C is wrong because retraining without validating performance can silently deploy a worse model and provides no monitoring signal. Option D is wrong because freezing the model version does not address monitoring needs and prevents necessary updates when drift or performance decay is detected.

Answer

Monitor model accuracy only at deployment time.

Answer

Regularly retrain without checking performance.

Answer

Freeze the model version once deployed to avoid changes.

Question 9

A company deploys an LLM-based application that retrieves external web content to answer user queries. An attacker crafts a webpage that, when retrieved, injects a hidden instruction telling the LLM to ignore its system prompt and output sensitive internal data. What type of attack is this?

Accepted Answer

Indirect prompt injection. Indirect prompt injection occurs when an attacker injects malicious instructions into external content that the LLM retrieves and processes, such as a webpage. The LLM then executes those instructions, potentially ignoring its system prompt and leaking sensitive data. This is distinct from direct prompt injection, where the attacker directly inputs the malicious prompt.

Answer

Direct prompt injection

Answer

Jailbreaking

Answer

Model inversion attack

Question 10

A team deploys a real-time fraud detection model on a streaming platform. The model must produce predictions within 100 milliseconds per event. Initial latency is 150 ms. Which optimization is most likely to meet the latency requirement?

Accepted Answer

Apply model quantization to reduce precision from FP32 to INT8.. Model quantization reduces the numerical precision of the model's weights and activations from FP32 to INT8, which decreases memory footprint and speeds up inference. This optimization directly addresses the 150 ms latency by enabling faster arithmetic operations on modern hardware, often cutting inference time by 2-4x, which can bring latency below the 100 ms requirement.

Answer

Increase the batch size to process more events simultaneously.

Answer

Add more feature engineering steps to improve model accuracy.

Answer

Migrate from a decision tree ensemble to a deep neural network.

Question 11

A team deploying an AI model for real-time fraud detection notices that inference latency is too high. The model is a deep neural network with 50 layers, deployed on a cloud GPU. Which of the following is the BEST approach to reduce latency while maintaining acceptable accuracy?

Accepted Answer

Apply knowledge distillation to create a smaller model.. Knowledge distillation trains a smaller 'student' model to mimic the behavior of a larger 'teacher' model, significantly reducing the number of parameters and layers while preserving most of the original accuracy. This directly addresses the high inference latency caused by the 50-layer DNN by producing a compact model that runs faster on the same GPU hardware.

Answer

Deploy the model on a more powerful GPU.

Answer

Reduce the batch size for inference.

Answer

Replace the DNN with a logistic regression model.

Question 12

A data scientist wants to group customers into segments based on purchasing behavior without predefined labels. Which type of machine learning is most appropriate?

Accepted Answer

Unsupervised learning. Unsupervised learning is the correct choice because the data scientist has no predefined labels and wants to discover natural groupings in customer purchasing behavior. Clustering algorithms, such as K-means or DBSCAN, are used in unsupervised learning to segment data based on inherent patterns without any target variable.

Answer

Reinforcement learning

Answer

Supervised learning

Answer

Semi-supervised learning

Question 13

An AI security analyst is evaluating a model that classifies images. The team wants to test whether small, imperceptible changes to input images can cause misclassification. Which type of attack are they testing?

Accepted Answer

Adversarial examples. Adversarial examples are specifically crafted inputs with small, imperceptible perturbations designed to cause a machine learning model to misclassify them. This directly matches the scenario of testing whether tiny changes to images can fool the classifier, which is a core concept in AI security for evaluating model robustness.

Answer

Data poisoning

Answer

Model inversion

Answer

Membership inference

Question 14

A model's training accuracy is 99% but validation accuracy drops to 60%. What is the most likely issue?

Accepted Answer

Overfitting. A training accuracy of 99% with a validation accuracy of only 60% is a classic symptom of overfitting. The model has memorized the training data, including noise and outliers, rather than learning generalizable patterns, causing it to perform poorly on unseen validation data.

Answer

Data leakage

Answer

Multicollinearity

Answer

Underfitting

Question 15

An organization uses a machine learning model to approve loans. The model shows higher false positive rates for a protected group. Which data engineering step should be taken to mitigate this?

Accepted Answer

Use adversarial debiasing technique. Adversarial debiasing is a technique that trains the model to minimize prediction error while simultaneously preventing an adversary from predicting the protected attribute from the model's outputs. This directly reduces disparate impact by forcing the model to learn representations that are uncorrelated with the protected group, thereby lowering false positive rates for that group without simply removing the attribute.

Answer

Remove the protected attribute from training data

Answer

Increase model complexity

Answer

Add synthetic data to balance groups

Question 16

Which TWO data preprocessing techniques reduce the dimensionality of a dataset?

Accepted Answer

Principal Component Analysis (PCA). Principal Component Analysis (PCA) (D) is correct because it projects the original features onto a smaller set of orthogonal principal components that capture most of the variance, thereby reducing the number of dimensions while preserving as much information as possible. Feature selection (E) is also correct because it explicitly removes irrelevant or redundant features and keeps only a subset of the original variables, directly lowering the dataset's dimensionality. By contrast, one-hot encoding (A) increases dimensionality by creating a separate binary column per category, and imputation (B) merely fills in missing values without changing the number of features. Feature scaling (C) standardizes or normalizes feature values (e.g., via min-max or z-score) but leaves the feature count unchanged, so it does not reduce dimensionality.

Answer

One-hot encoding

Answer

Imputation

Answer

Feature scaling

Question 17

A financial institution uses an AI model to approve loans. The model uses features including credit score and ZIP code. During an audit, it is discovered that the model has a high false positive rate for loan default predictions in certain ZIP codes. What should the institution do to address this?

Accepted Answer

Retrain the model with fairness constraints. Retraining the model with fairness constraints directly addresses the root cause of the bias—the model's learned correlations between ZIP code and default risk. Fairness constraints, such as demographic parity or equalized odds, are applied during training to ensure the model's predictions are not systematically skewed against certain groups. This approach preserves the predictive power of legitimate features while mitigating discriminatory outcomes, aligning with AI governance principles.

Answer

Remove the ZIP code feature from the model

Answer

Increase the decision threshold for those ZIP codes

Answer

Discontinue use of the model for those ZIP codes

Question 18

A social media company's AI recommendation system pushes extreme content to users, causing harm. Which ethical principle is most violated?

Accepted Answer

Non-maleficence. Non-maleficence (do no harm) is the principle most directly violated because the AI system actively causes harm by pushing extreme content that damages users' mental health or incites harmful behavior. Unlike beneficence (doing good), non-maleficence focuses on avoiding harm, and the system's design fails to prevent foreseeable negative outcomes.

Answer

Autonomy

Answer

Justice

Answer

Beneficence

Question 19

Which THREE of the following are key principles of trustworthy AI as defined by major regulatory bodies?

Accepted Answer

Fairness and non-discrimination. Fairness and non-discrimination (A) is a core principle because trustworthy AI frameworks such as the EU AI Act and OECD AI Principles require systems to avoid biased outcomes and unjust discrimination across protected groups. Transparency and explainability (B) is also correct, as these bodies mandate that AI decisions be understandable and that stakeholders can access meaningful information about how systems operate. Accountability (E) is correct because trustworthy AI requires clear responsibility and redress mechanisms, ensuring that developers and deployers can be held answerable for system outcomes. Maximum profitability (C) is not a trustworthiness principle; it is a business objective and is not part of regulatory AI ethics definitions. Proprietary secrecy (D) is also not a principle, since trustworthiness frameworks emphasize disclosure, auditability, and transparency rather than concealment.

Answer

Maximum profitability

Answer

Proprietary secrecy

Question 20

An organization wants to implement an AI ethics board. Which composition best ensures independence and expertise?

Accepted Answer

Mix of internal stakeholders and external ethicists. An AI ethics board must combine internal stakeholders (who understand organizational context, data flows, and operational constraints) with external ethicists (who provide independent, unbiased perspectives and specialized knowledge of ethical frameworks like IEEE Ethically Aligned Design or the EU AI Act). This composition ensures the board can evaluate AI systems for bias, fairness, and transparency without being dominated by business or technical interests, which is critical for maintaining trust and regulatory compliance.

Answer

All members from the legal department

Answer

IT department head and data scientists

Answer

Only senior executives from the company