AI0-001 Practice Test 42 — 15 Questions

Question 1

An AI operations team is monitoring a deployed image classification model. They notice a gradual increase in prediction confidence but a drop in accuracy. Which THREE actions should they take to diagnose the issue?

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

Accepted Answer

Analyze the model's calibration curve to see if confidence scores align with actual accuracy.. A calibration curve (reliability diagram) directly compares predicted confidence scores against actual accuracy. In this scenario, increasing confidence with dropping accuracy indicates miscalibration—the model is becoming overconfident. Analyzing the calibration curve reveals whether the confidence scores systematically deviate from true probabilities, which is the core diagnostic step for this specific symptom.

Answer

Increase the size of the training dataset by collecting more unlabeled data.

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Retrain the model immediately with the most recent data.

Question 2

A company is building an AI-powered document intelligence system to extract key fields from scanned invoices. The data contains 95% of invoices from one vendor and 5% from others. During model training, the F1 score is 0.95 on the overall test set, but the performance on the minority vendor invoices is very poor. What is the MOST likely cause?

Accepted Answer

The dataset is imbalanced, and the model is biased toward the majority class. The imbalanced dataset causes the model to learn mostly from the majority class, leading to poor performance on the minority class. The other options are less likely given the high overall F1 score.

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The model is overfitting on the minority class

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The data has a train/test leakage problem

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The feature extraction is incorrect for the minority vendor invoices

Question 3

A team fine-tunes a 7B parameter LLM using LoRA on a custom instruction dataset. After training, they observe that the model's outputs are only marginally different from the base model. Which is the MOST likely cause?

Accepted Answer

The LoRA rank was set too low (e.g., r=1), limiting the adapter's capacity to learn the task. LoRA has a rank hyperparameter that controls adapter expressiveness. If the rank is too low, the adapter cannot capture the desired task. Other hyperparameters like learning rate affect convergence but rank directly impacts capacity.

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The dataset contained too many examples, overfitting the adapter

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The base model was too small to benefit from fine-tuning

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The learning rate was too high, causing the model to diverge

Question 4

During a red-team exercise on an AI model, testers successfully extracted training data. Which vulnerability is this?

Accepted Answer

Model inversion. Model inversion attacks allow an adversary to reconstruct training data by exploiting the model's learned representations. In this scenario, the testers successfully extracted training data, which is the hallmark of a model inversion attack, not just inferring membership.

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Membership inference

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Adversarial example

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Data poisoning

Question 5

A company uses AI to generate marketing images. They want to ensure that the images are clearly identified as AI-generated to comply with transparency obligations. Which approach is most effective?

Accepted Answer

Embed a visible watermark stating 'AI-generated' in each image. Watermarking AI-generated content with visible or invisible markers is a direct way to disclose AI origin. Using metadata alone can be stripped; disclaimers in terms of service are not visible to users; relying on human detection is unreliable.

Answer

Add a disclaimer in the platform's terms of service

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Include metadata in the image file indicating it is AI-generated

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Rely on deepfake detection algorithms to flag the images

Question 6

An AI model is being developed for medical diagnosis from X-ray images. The dataset contains only frontal chest X-rays. The model achieves high accuracy on test set but fails on lateral views. What is the most likely cause?

Accepted Answer

Dataset bias. The model was trained exclusively on frontal chest X-rays, so it never learned features specific to lateral views. When tested on lateral views, the distribution shift causes poor performance, which is a classic case of dataset bias (sampling bias). The high accuracy on the test set is misleading because the test set also only contained frontal views, masking the model's inability to generalize to other X-ray orientations.

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Underfitting

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Label noise

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Overfitting

Question 7

In the AI lifecycle, which phase involves splitting data into training, validation, and test sets?

Accepted Answer

Data preprocessing. Data preprocessing is the phase where raw data is cleaned, transformed, and prepared for modeling. Splitting the dataset into training, validation, and test sets is a critical step during this phase to ensure unbiased evaluation and prevent data leakage. This split occurs before any model training begins, making it part of preprocessing rather than training or evaluation.

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Model training

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Data collection

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Model evaluation

Question 8

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.

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Data leakage

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Multicollinearity

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Underfitting

Question 9

A government agency is deploying an AI model to screen loan applications. The model uses features like income, credit score, employment history, and zip code. During fairness auditing, the model is found to deny a disproportionately high number of applicants from a particular demographic group, even when controlling for legitimate financial factors. The agency wants to mitigate this bias without significantly reducing overall accuracy. Which approach should the data scientist prioritize?

Accepted Answer

Use adversarial debiasing during model training. Adversarial debiasing is the correct approach because it directly optimizes the model to remove sensitive information (e.g., demographic group membership) from its internal representations while preserving predictive accuracy. This technique trains a primary model to predict the target (loan approval) and an adversary to predict the protected attribute from the model's learned features, forcing the primary model to learn representations that are both accurate and unbiased. It addresses the root cause of bias—correlation between protected attributes and model predictions—without requiring post-hoc threshold adjustments or sacrificing overall performance.

Answer

Adjust the decision threshold for the affected group

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Remove the zip code feature from the model

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Apply sample weighting to balance the demographic groups

Question 10

An AI chatbot is being developed to assist with customer support. The team is concerned about prompt injection attacks where malicious users try to override the system's instructions. Which defense is MOST effective against direct prompt injection?

Accepted Answer

Input validation and sanitization. Input validation and sanitization can detect and block attempts to inject malicious instructions. While guardrails help, the primary defense is to filter and validate user inputs before they reach the model.

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Rate limiting API requests

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Output filtering

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Using a larger model

Question 11

A data engineer needs to store training data in a format that supports columnar pruning during model training. Which storage format should they use?

Accepted Answer

Parquet. Parquet is the correct choice because it is a columnar storage format that enables column pruning, allowing the training process to read only the columns needed for model training rather than entire rows. This reduces I/O and speeds up data loading, which is critical for large-scale AI/ML workloads. Unlike row-oriented formats, Parquet stores data by columns, making it efficient for analytical queries and feature selection.

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XML

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JSON

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CSV

Question 12

An organization is deploying an AI agent that uses the ReAct pattern to answer customer queries by calling external APIs. Which THREE components are essential in this agentic workflow?

Accepted Answer

A planning algorithm to decompose tasks. The ReAct pattern interleaves reasoning traces with tool calls (function calling), enabling the agent to plan and execute actions step by step. Tool use and multi-step reasoning are core to the pattern.

Answer

Fine-tuning the LLM on domain-specific data

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A vector store for long-term memory

Question 13

A company built a speech-to-text model using a recurrent neural network (RNN). During deployment, the model performs poorly on accented speech. Which action would most effectively improve model robustness?

Accepted Answer

Augment the training dataset with various accented audio samples and retrain the model.. Augmenting the training dataset with diverse accented audio samples directly addresses the root cause of poor performance—distribution shift between training and deployment data. Retraining the model on this enriched dataset allows the RNN to learn invariant features across accents, improving generalization without altering the model architecture or risking catastrophic forgetting from fine-tuning on a tiny sample.

Answer

Collect a small sample of accented speech and fine-tune the model on that sample only.

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Add dropout and reduce the number of RNN layers to prevent overfitting to the current data.

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Replace the RNN with a convolutional neural network (CNN) for feature extraction.

Question 14

Which AI governance framework is specifically designed by the U.S. National Institute of Standards and Technology (NIST) to help organizations manage AI risks?

Accepted Answer

NIST AI Risk Management Framework. The NIST AI Risk Management Framework (AI RMF) is the specific governance framework developed by the U.S. National Institute of Standards and Technology to help organizations manage AI risks, including those related to trustworthiness, fairness, and robustness. It provides a structured approach for identifying, assessing, and mitigating risks throughout the AI lifecycle, aligning with NIST's role in setting standards for cybersecurity and risk management.

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ISO/IEC 27001

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COBIT

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GDPR

Question 15

A company is building a RAG-based Q&A system for a large collection of technical manuals. They need to choose an embedding model and a similarity search method. Which TWO choices are most appropriate for this scenario? (Select TWO)

Accepted Answer

Use a domain-specific embedding model fine-tuned on technical documentation. Cosine similarity is the standard metric for comparing embeddings (normalized vectors). Using a domain-specific embedding model (e.g., fine-tuned on technical text) yields better retrieval accuracy. Dot product can be used but is less common; Euclidean distance is not ideal for high-dimensional embeddings; a general-purpose model may perform poorly on domain-specific language.

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Use a general-purpose embedding model like text-embedding-ada-002

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Use dot product as the similarity metric for non-normalized embeddings

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Use Euclidean distance as the similarity metric for vector search