AI0-001 Practice Test 43 — 15 Questions

Question 1

A financial services company is deploying a text-generation model that drafts internal reports. To reduce the risk of the model memorizing and later reproducing personally identifiable information from its fine-tuning dataset, the security team wants to add noise to the training process in a way that provides a mathematical privacy guarantee. Which approach should they implement?

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

Accepted Answer

Use differential privacy with a calibrated noise multiplier during training.. Differential privacy is the only listed technique that provides a formal, quantifiable guarantee that any single training record has limited influence on the model. By adding calibrated noise during training, the organization bounds memorization of PII in the report-drafting model, which directly mitigates the extraction risk. The other controls either reduce overfitting indirectly, obscure identifiers without a guarantee, or protect data only at rest.

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Hash all personally identifiable information in the fine-tuning corpus before training.

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Encrypt the fine-tuning dataset at rest and enforce role-based access to the storage bucket.

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Apply L2 regularization to the model weights during fine-tuning.

Question 2

A developer is building an AI agent that needs to call external APIs (e.g., get weather, send email) based on user requests. Which pattern is BEST for enabling the agent to autonomously decide when to call these APIs?

Accepted Answer

Implement function calling in the LLM to generate structured API calls. Function calling in the LLM allows the model to generate structured API calls (e.g., JSON) based on user requests, enabling the agent to autonomously decide when and which API to call. This pattern is best because it leverages the LLM's reasoning to select appropriate functions and parameters, integrating seamlessly with external systems.

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Hard-code the API calls in the agent's logic

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Use a chain-of-thought prompt to reason about the steps

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Use a planning agent with a predefined workflow

Question 3

An AI engineer is selecting a PEFT technique to fine-tune a large language model. Which TWO are examples of PEFT (Parameter-Efficient Fine-Tuning)?

Accepted Answer

LoRA. LoRA (Low-Rank Adaptation) is correct because it freezes the pretrained model weights and injects trainable low-rank decomposition matrices into the transformer layers, updating only a tiny fraction of parameters. QLoRA is correct because it extends LoRA by quantizing the base model to 4-bit (NF4) and adding trainable low-rank adapters, further reducing memory while remaining a parameter-efficient method. Instruction tuning (A) is a training objective/paradigm that typically updates all or many parameters, not a PEFT technique itself. Gradient checkpointing (D) is a memory-saving trick that recomputes activations during backpropagation and does not reduce the number of trainable parameters. Full fine-tuning (E) updates every model parameter, which is the opposite of parameter-efficient fine-tuning.

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Instruction tuning on a large dataset

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Gradient checkpointing

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Full fine-tuning of all parameters

Question 4

A social media platform uses an AI system to moderate content. The system incorrectly flags legitimate posts as hate speech, disproportionately affecting minority groups. Which type of bias is likely present?

Accepted Answer

Algorithmic bias. The AI system's output (incorrectly flagging legitimate posts as hate speech) is a direct result of the model's design, training data, or deployment choices, which is the definition of algorithmic bias. This bias disproportionately affects minority groups because the algorithm's decision-making process systematically produces unfair outcomes for those groups, even if the training data itself was not historically biased.

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Historical bias

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Selection bias

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Confirmation bias

Question 5

A deep learning model for image classification achieves 99% training accuracy but only 85% validation accuracy. The model has millions of parameters. Which technique is most likely to reduce overfitting while maintaining high accuracy?

Accepted Answer

Add dropout layers with a rate of 0.5 after each convolutional block. Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents co-adaptation of features and forces the network to learn more robust representations. With 99% training accuracy and 85% validation accuracy, the model is clearly overfitting, and adding dropout layers with a rate of 0.5 after each convolutional block directly addresses this by reducing the model's capacity to memorize the training data, while still allowing high accuracy on the validation set.

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Reduce batch size from 32 to 8

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Decrease the learning rate by a factor of 10

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Increase the number of training epochs to 500

Question 6

A company uses a cloud-based ML platform to train a model and wants to deploy it for real-time inference. They also need to monitor the endpoint for data drift and retrain automatically. Which feature enables this automated retraining pipeline?

Accepted Answer

ML Pipeline Orchestration. ML Pipeline Orchestration is the correct answer because it provides a fully managed service for creating, automating, and managing end-to-end machine learning workflows. It allows you to define a pipeline that includes steps for monitoring data drift (via a model monitoring service), triggering retraining jobs, and deploying updated models, enabling the automated retraining pipeline described in the question.

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Model Debugging

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Data Labeling Service

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Model Monitoring

Question 7

A company deployed an AI chatbot that started generating offensive responses after a data update. The security team needs to quickly mitigate the issue. What should they do first?

Accepted Answer

Disable the chatbot and investigate. The first priority when an AI chatbot generates offensive responses is to stop the harm immediately. Disabling the chatbot (Option B) halts all user interactions, preventing further offensive outputs while the security team investigates the root cause. This aligns with the principle of containment before remediation in incident response.

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Delete the training data

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Roll back to previous model version

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Add a content filter

Question 8

A startup has developed a natural language processing model for sentiment analysis. Their CI/CD pipeline includes a step that runs unit tests on the model's output format and a validation step that checks accuracy on a static test dataset. Recently, the pipeline often fails during the validation step, but the failures are inconsistent—sometimes the same model version passes, sometimes fails. The team suspects the test dataset is small and randomly sampled. They need a reliable validation process to deploy models with confidence. Which approach should the team implement?

Accepted Answer

Fix the test dataset to be larger and more representative, and use a statistical test to compare against baseline. The core issue is that the static test dataset is too small and randomly sampled, leading to inconsistent validation results. By fixing the dataset to be larger and more representative, and using a statistical test (e.g., a paired t-test or McNemar's test) to compare the model's accuracy against a baseline, the team can reliably determine if performance changes are statistically significant, eliminating the randomness that causes pipeline failures to be inconsistent.

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Replace the static test set with k-fold cross-validation in each pipeline run

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Increase the accuracy threshold to 95% so only very good models pass

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Remove the validation step and rely on unit tests only

Question 9

A team is deploying a sentiment analysis model for social media posts. The model currently performs well on English text but poorly on code-switched text (e.g., Spanglish). Which approach is MOST effective for improving performance on code-switched data without starting from scratch?

Accepted Answer

Fine-tune the existing model on a corpus of code-switched text. Fine-tuning the existing model on a corpus of code-switched text adapts the model to the new language pattern efficiently.

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Use a larger base model without additional training

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Apply data augmentation by translating all code-switched posts to English

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Train a new model from scratch on a mix of English and code-switched data

Question 10

A data scientist is using LIME to explain a black-box model. Which TWO characteristics of LIME are true?

Accepted Answer

It creates an interpretable surrogate model locally around a prediction. Option D is correct because LIME (Local Interpretable Model-agnostic Explanations) works by perturbing the input around a specific prediction and fitting a simple, interpretable surrogate model (such as a linear model or decision tree) that approximates the black-box model's behavior in that local neighborhood. Option E is correct because LIME is model-agnostic: it only requires the ability to query the model's predictions (input-output access), so it can be applied to any machine learning model, including neural networks, SVMs, and gradient-boosted trees. Option A is not correct because LIME explains which features drove a prediction, not the model's confidence or probability calibration. Option B is not correct because LIME treats the model as a black box and does not need internal parameters or gradients. Option C is not correct because LIME produces local explanations for individual predictions, not a global feature-importance ranking across the entire dataset.

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It provides a measure of model confidence in its prediction

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It requires access to the model's internal parameters

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It provides a global ranking of feature importance across the entire dataset

Question 11

An ML team monitors a production model using a dashboard that shows daily performance metrics. Over the past month, the model's accuracy has dropped from 92% to 87%, while the data distribution of input features has remained stable according to statistical tests. Which type of model drift is most likely occurring?

Accepted Answer

Concept drift. Concept drift occurs when the relationship between input features and the target variable changes, even if the input data distribution remains stable. In this scenario, the model's accuracy declines from 92% to 87% while input feature distributions are unchanged, indicating that the underlying mapping from features to labels has shifted—a classic sign of concept drift.

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Data drift (covariate shift)

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Model decay

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Overfitting

Question 12

A model serving endpoint is tested using curl commands. Based on the exhibit, what is the most likely issue?

Accepted Answer

The model is experiencing intermittent high latency leading to timeouts. The exhibit shows that the first curl request succeeds (HTTP 200), but subsequent requests fail with 'curl: (28) Operation timed out' after the default timeout of 30 seconds. This pattern of intermittent success followed by timeouts is characteristic of a model experiencing high latency spikes, not a persistent server error or configuration issue. The server is reachable and the model responds correctly some of the time, ruling out deployment or malformed input issues.

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The server is returning HTTP 500 errors

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The input features are malformed

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The model is not deployed on the server

Question 13

Under the EU AI Act, an AI system used for credit scoring is classified as high-risk. Which THREE obligations apply to the deployer of such a system?

Accepted Answer

Provide transparency information to affected individuals. Article 13 of the EU AI Act requires deployers of high-risk AI systems to provide clear and meaningful transparency information to affected individuals, including the system's capabilities, limitations, and the logic behind decisions. This obligation ensures that individuals subject to automated credit scoring understand how their data is being used and can exercise their rights under the regulation.

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Register the system with a central EU database

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Publish the model's source code publicly

Question 14

A team is designing a secure API for an AI model. They want to prevent data leakage through overly detailed error messages. Which principle should they follow?

Accepted Answer

Use generic error messages. Least-privilege API access and minimal error information reduce the attack surface. Specifically, returning generic error messages prevents leaking internal details.

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Return detailed error codes for debugging

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Log errors to the client side

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Disable all error messages

Question 15

A company deploys an AI resume screening tool. It learns from historical hiring data where most successful hires were male, leading the model to favour male candidates. Which type of bias is this primarily?

Accepted Answer

Historical bias. The model learned from historical hiring data that already contained a gender imbalance, where most successful hires were male. This is a classic case of historical bias, where the training data reflects past societal or organizational biases, and the AI system perpetuates those biases in its predictions. The bias originates in the data, not in the model's algorithm or the sampling method.

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Confirmation bias

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Algorithmic bias

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Selection bias