AI0-001 Practice Test 10 — 10 Questions

Question 1

A manufacturing company uses a computer vision AI to inspect products on an assembly line for defects. The AI model was trained on images from a single camera angle under bright, uniform lighting. Recently, the company moved the inspection station to a different part of the factory where lighting is dimmer and varies due to nearby windows. The model now misclassifies many non-defective products as defective, causing false alarms and production delays. The team has limited labeled data from the new environment. Which action should the team take to restore inspection accuracy while minimizing downtime?

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

Accepted Answer

Apply domain adaptation techniques using a small set of labeled images from the new environment. Domain adaptation techniques allow a model trained on a source domain (bright, uniform lighting) to generalize to a target domain (dim, variable lighting) using only a small set of labeled images from the new environment. This approach minimizes downtime because it avoids the need for large-scale data collection or retraining from scratch, and it directly addresses the distribution shift that causes false positives.

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Increase the defect classification threshold to reduce false positives

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Revert to the previous lighting setup by reinstalling bright, uniform lights

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Retrain the model from scratch using a large dataset of images from the new environment

Question 2

A team is building an AI-powered recommendation system for an e-commerce platform. They want to test the system before deployment. Which TWO types of testing are MOST relevant for this AI system? (Select TWO)

Accepted Answer

Evaluation frameworks for model output quality. Option C (Evaluation frameworks for model output quality) is correct because AI/ML systems produce probabilistic outputs that cannot be validated by traditional assertions; frameworks such as accuracy, precision/recall, F1, BLEU, or ROUGE metrics are needed to assess whether the recommendation model generates relevant, high-quality predictions before deployment. Option D (Unit tests for data pipelines) is correct because the recommendation system depends on feature engineering and ETL/data ingestion pipelines; unit tests verify transformations, schema conformance, null handling, and data integrity so that garbage data does not silently degrade model behavior. Option A (Load testing the web server) is not selected because it measures infrastructure throughput and concurrency rather than the AI system's model correctness or data quality. Option B (Integration tests for API calls) is not selected because, while useful generally, it tests service-to-service communication rather than the AI-specific concerns of model output quality and data pipeline correctness. Option E (Regression testing on the UI) is not selected because it validates front-end behavior and visual consistency, which is peripheral to testing the AI model and its data pipeline.

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Load testing the web server

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Integration tests for API calls

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Regression testing on the UI

Question 3

A team is developing an AI agent to assist users with multi-step tasks such as booking a flight, reserving a hotel, and scheduling a car rental. The agent needs to reason about the order of steps and handle dependencies. Which pattern is BEST suited?

Accepted Answer

ReAct pattern (Reasoning and Acting). The ReAct pattern (Reasoning and Acting) is best suited because it interleaves reasoning traces with tool calls, allowing the agent to dynamically plan and adjust steps based on intermediate results. For multi-step tasks with dependencies (e.g., booking a flight before a hotel), ReAct enables the agent to reason about order, handle failures, and call external APIs step-by-step, which is essential for robust task completion.

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Simple tool use without reasoning

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Using a single prompt with all instructions

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Fine-tuning a model to output all steps at once

Question 4

A retail bank has deployed a credit-risk scoring model as a REST endpoint behind an API gateway. The model was trained on data from 2019–2023. Compliance now requires the bank to detect when input feature distributions drift away from the training baseline and to trigger retraining before approval rates degrade. Which approach should the bank implement FIRST?

Accepted Answer

Enable population stability index (PSI) monitoring on each input feature against the stored training distribution and alert when PSI exceeds a set threshold.. Covariate drift is detected by comparing live inference feature distributions with the training baseline, and population stability index is the established metric for tabular models. It operates without ground-truth labels, so it fires early, before approval-rate decay confirms the problem. Fine-tuning on unlabeled traffic, autoscaling, or threshold manipulation all fail to measure distributional change and therefore cannot satisfy the compliance requirement to trigger retraining.

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Lower the classification threshold from 0.50 to 0.40 so more applicants are approved and the approval rate stays stable.

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Continuously fine-tune the deployed model on every new loan application that arrives at the endpoint.

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Increase the API gateway rate limit and add horizontal replicas so the endpoint can absorb higher application volume.

Question 5

A team is designing a deep learning pipeline for a computer vision task. They want to reduce overfitting. Which two techniques are specifically effective for this purpose? (Select TWO.)

Accepted Answer

Dropout. Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the network from relying too heavily on any single neuron and forces it to learn more robust features. This reduces overfitting by introducing noise that improves generalization.

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Using a smaller batch size

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Adding more layers

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Increasing the learning rate

Question 6

A data scientist trains a deep learning model on a large dataset. The training loss decreases steadily but the validation loss starts increasing after 20 epochs. The scientist uses early stopping with patience=5. Which of the following is the MOST likely cause and best corrective action?

Accepted Answer

Model is overfitting; add dropout regularization.. The training loss decreasing while validation loss increasing after 20 epochs is a classic sign of overfitting, where the model memorizes training data noise instead of generalizing. Early stopping with patience=5 would halt training after 5 epochs of no validation improvement, but the root cause is overfitting. Adding dropout regularization randomly drops neurons during training, forcing the network to learn more robust features and reducing overfitting.

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Training data is not representative; collect more data.

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Model is underfitting; increase model capacity.

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Learning rate too high; reduce learning rate.

Question 7

An AI security team is mapping threats specific to their ML pipeline using the STRIDE framework. Which threat category is primarily addressed by ensuring that training data is not tampered with?

Accepted Answer

Tampering. Ensuring that training data is not tampered with directly addresses the Tampering threat category in the STRIDE framework. Tampering involves the unauthorized modification of data, and in an ML pipeline, corrupted training data can lead to model poisoning, where the model learns incorrect patterns or backdoors. By protecting the integrity of the training dataset, the team mitigates the risk of adversarial manipulation that could degrade model performance or introduce vulnerabilities.

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Spoofing

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Repudiation

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Information disclosure

Question 8

A data scientist is tuning hyperparameters for a support vector machine (SVM) with an RBF kernel. Which two hyperparameters most significantly affect model performance? (Select TWO.)

Accepted Answer

gamma (kernel coefficient). Gamma defines the influence of a single training example, with low values meaning a far reach and high values meaning a close reach. C controls the trade-off between achieving a low error on the training data and minimizing the margin, directly impacting overfitting. Together, they are the two most critical hyperparameters for an SVM with an RBF kernel.

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learning rate

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epsilon (for epsilon-SVR)

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degree (for polynomial kernel)

Question 9

A company wants to deploy a machine learning model that requires continuous learning as new data arrives. The model must be able to adapt to changing patterns without retraining from scratch. Which approach should be used?

Accepted Answer

Online learning. Online learning (also called incremental learning) updates the model incrementally as each new data point arrives, without requiring full retraining. This makes it ideal for scenarios where data arrives continuously and patterns shift over time, as the model can adapt its parameters on the fly.

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Transfer learning

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Batch learning

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Unsupervised learning

Question 10

A product team wants a system that can generate high-quality synthetic images of furniture in different room settings for an online catalog. The images must be photorealistic and vary in style. Which generative AI approach is BEST suited for this task?

Accepted Answer

Diffusion model. Diffusion models are the best choice because they iteratively denoise random noise to produce high-quality, photorealistic images with diverse styles. Unlike GANs, they avoid mode collapse and training instability, and they generate more detailed and varied outputs than VAEs, making them ideal for furniture catalog images in different room settings.

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Variational autoencoder (VAE)

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Generative adversarial network (GAN)

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Recurrent neural network (RNN)