AI0-001 Practice Test 23 — 15 Questions

Question 1

An organization is deploying a deep learning model in production. Which THREE components are essential for maintaining model performance over time?

Accepted Answer

Performance monitoring. Performance monitoring (A) is essential because it provides continuous visibility into model metrics such as accuracy, latency, and throughput, enabling early detection of degradation. Without ongoing monitoring, teams cannot identify when a model's predictions deviate from expected behavior, which is critical for maintaining reliability in production.

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Hyperparameter tuning

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Feature importance analysis

Question 2

A financial services firm has deployed an AI model for real-time credit scoring. The operations team needs to ensure the model remains reliable and compliant over time. Which TWO actions should the team prioritize? (Choose two.)

Accepted Answer

Implement automated monitoring for data drift and model performance metrics.. Automated monitoring for data drift and model performance metrics is essential for maintaining reliability and compliance in a real-time credit scoring system. Data drift detection (e.g., using population stability index or KL divergence) alerts the team when input distributions shift, which could degrade model accuracy and lead to non-compliant decisions. Continuous monitoring of metrics like AUC, precision, and recall ensures the model stays within regulatory thresholds without manual intervention.

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Deploy a model versioning system with automated rollback capabilities.

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Schedule monthly manual retraining of the model using historical data.

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Generate weekly compliance reports for regulatory review.

Question 3

A data scientist trains a linear regression model to predict house prices. The model has high bias and low variance. Which action would most likely reduce bias?

Accepted Answer

Add polynomial features. High bias indicates the model is underfitting the data, meaning it is too simple to capture the underlying patterns. Adding polynomial features increases model complexity by introducing non-linear terms, which allows the linear regression model to better fit the training data and thus reduce bias.

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Apply L2 regularization

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Increase the training dataset size

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Remove irrelevant features

Question 4

A team is deploying a generative AI model for a real-time customer-facing application. They need to balance cost and latency. Which deployment strategy is MOST suitable?

Accepted Answer

AI microservices with streaming responses and async processing queues. AI microservices with streaming responses and async processing queues decouple inference from the request lifecycle, allowing the system to handle variable loads efficiently while maintaining low latency for real-time interactions. This architecture balances cost by scaling only the necessary components (e.g., GPU-backed inference services) and uses streaming (e.g., Server-Sent Events or WebSockets) to deliver partial results, reducing perceived latency for the customer.

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Monolithic API with serverless functions

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Edge deployment on user devices

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Batch processing with synchronous requests

Question 5

A DevOps team is deploying a machine learning model using a CI/CD pipeline. They want to ensure the model is reproducible and traceable. Which TWO practices should they implement?

Accepted Answer

Version the training dataset and code using Git and DVC.. Versioning the training dataset and code using Git and DVC (Data Version Control) ensures that every model training run can be exactly reproduced by tracking changes to both the source code and the data. DVC stores metadata pointers in Git while the actual data is stored in a remote cache, enabling precise rollback and audit trails. This practice directly supports reproducibility and traceability, which are core requirements for MLOps and CI/CD pipelines.

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Manually deploy the model to production after approval.

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Store only the final model artifact in a shared drive.

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Use a spreadsheet to record model version numbers.

Question 6

A machine learning engineer needs to choose an algorithm for grouping customers into segments based on purchasing behavior without any labels. Which algorithm should the engineer use?

Accepted Answer

K-means clustering. K-means clustering is an unsupervised learning algorithm that groups unlabeled data into clusters based on feature similarity, making it ideal for segmenting customers by purchasing behavior without predefined labels. It partitions data into K clusters by minimizing within-cluster variance, which directly addresses the requirement of discovering natural groupings in the data.

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Random forest classifier

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Linear regression

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Support vector machine

Question 7

An organization wants to ensure its AI systems comply with new regulations requiring explanations for automated decisions. Which governance practice is most directly relevant?

Accepted Answer

Deploying explainability tools. Deploying explainability tools (B) is the most directly relevant governance practice because the regulation specifically requires explanations for automated decisions. Explainability tools, such as LIME or SHAP, generate human-interpretable justifications for model outputs, enabling compliance with transparency mandates. This directly addresses the need to understand and communicate why a particular decision was made, unlike other practices that focus on privacy, fairness, or oversight.

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Implementing differential privacy

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Conducting bias audits

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Establishing an AI ethics board

Question 8

A data scientist is training a binary classification model to detect fraudulent transactions. The dataset is highly imbalanced with 99% legitimate and 1% fraudulent. Which evaluation metric should be prioritized to assess model performance?

Accepted Answer

F1-score. In a highly imbalanced dataset (99% legitimate, 1% fraudulent), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy without detecting any fraud. The F1-score combines precision and recall into a single metric, making it the preferred choice for evaluating binary classification performance on imbalanced data, as it penalizes both false positives and false negatives equally.

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Accuracy

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Mean Squared Error

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Log Loss

Question 9

A data scientist discovers that a model trained to predict loan defaults is denying loans at a higher rate for a particular demographic group. Which type of bias is MOST likely present?

Accepted Answer

Historical bias. Historical bias occurs when the training data reflects past societal inequalities, leading the model to learn and perpetuate those patterns. In this case, if historical loan data shows higher denial rates for a demographic group due to past discriminatory practices, the model will replicate that bias in its predictions. This is the most likely cause because the model is not inherently biased but inherits bias from the data it was trained on.

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Confirmation bias

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Selection bias

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Algorithmic bias

Question 10

A company deploys an AI system for loan approvals. The EU AI Act classifies this as high-risk. Which human oversight requirement applies?

Accepted Answer

Human-in-the-loop (HITL) mechanism. The EU AI Act requires human-in-the-loop (HITL) oversight for high-risk AI systems like loan approval, meaning a human must be able to intervene and override the system's decisions during operation. This ensures that automated decisions can be reviewed and corrected in real-time, preventing fully autonomous outcomes in critical areas such as credit scoring.

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Human-in-command approach

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Human-on-the-loop oversight

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Automated decision-making without human review

Question 11

A company is required to disclose that content has been generated or significantly modified by AI. Which practice directly addresses this transparency obligation?

Accepted Answer

Applying AI watermarking. AI watermarking directly addresses the transparency obligation by embedding a detectable signal into AI-generated content, enabling clear disclosure that the content was produced or significantly modified by AI. This practice aligns with regulatory requirements for provenance and traceability, as watermarks can be verified by automated systems or human inspection to confirm AI origin.

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Using LIME for explanations

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Implementing federated learning

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Publishing a model card

Question 12

An AI team is concerned about their model leaking sensitive information from its training data when queried. Which privacy-preserving technique adds noise to the training process to limit what can be inferred about any individual record?

Accepted Answer

Differential privacy. Differential privacy (A) is the correct answer because it directly addresses the concern of leaking sensitive information from training data by adding calibrated noise to the training process or query responses. This noise ensures that the output of the model does not significantly change whether any single individual's record is included or excluded, thereby limiting what can be inferred about any specific record. The technique is formalized through a privacy budget (ε, epsilon) that quantifies the privacy guarantee, making it the standard approach for privacy-preserving machine learning.

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Homomorphic encryption

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Data sanitization

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Federated learning

Question 13

A data governance team is developing an AI policy for a large corporation. Which TWO elements are essential for a responsible AI governance framework?

Accepted Answer

AI ethics board. An AI ethics board is essential for a responsible AI governance framework because it provides human oversight, ethical review, and accountability for AI decisions. This board ensures that AI initiatives align with corporate values, legal requirements, and ethical principles, such as fairness, transparency, and non-discrimination. Without an ethics board, there is no formal mechanism to challenge biased models or approve high-risk AI use cases.

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GPU cluster monitoring

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Single-model strategy

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Automated model retraining pipeline

Question 14

A company is deploying an AI model to recommend products. The model's training data included historical purchases from the past two years, but the business environment has changed significantly due to a market shift. What is the most likely issue affecting model performance?

Accepted Answer

Concept drift. Concept drift occurs when the statistical properties of the target variable change over time, degrading model performance. In this scenario, the market shift alters customer purchasing patterns, making the historical training data (from the past two years) no longer representative of current behavior. This is the most likely issue because the model's recommendations will be based on outdated correlations.

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Overfitting

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Underfitting

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Data leakage

Question 15

A company is building an AI-based resume screening tool. They want to ensure the system is secure against data poisoning attacks during the training phase. Which THREE of the following are appropriate defensive measures?

Accepted Answer

Use robust statistical methods (e.g., trimmed mean) that are less sensitive to outliers. Robust statistical methods like trimmed mean reduce the influence of outlier data points that could be injected by an adversary during training. By discarding extreme values, the model becomes less sensitive to poisoned samples, which is a key defense against data poisoning attacks that aim to corrupt the learned parameters.

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Apply input sanitization to inference-time queries

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Implement differential privacy during model training