AI0-001 Practice Test 11 — 15 Questions

Question 1

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?

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

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 2

A team is deploying an AI model for credit approval. Which TWO ethical considerations must be addressed?

Accepted Answer

Model interpretability. Model interpretability (B) is essential for credit approval because financial decisions must be explainable to regulators and customers under laws like GDPR or ECOA. A black-box model that cannot justify why a loan was denied violates compliance requirements, making interpretability a core ethical and legal necessity.

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Training speed

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Model accuracy

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Model size

Question 3

A developer is implementing a RAG system and needs to chunk large legal documents. The documents contain nested clauses and cross-references that should not be split across chunks. Which chunking strategy is MOST suitable?

Accepted Answer

Semantic chunking based on sentence and paragraph boundaries. Semantic chunking uses sentence boundaries or natural breakpoints, preserving logical units.

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Random chunking with varying sizes

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Hierarchical chunking combining small and large chunks

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Fixed-size chunking with 256 tokens

Question 4

A data scientist notices that a hiring model systematically scores female candidates lower than male candidates with similar qualifications. The training data was collected from past hiring decisions where the company historically hired more men. Which type of AI bias is most directly demonstrated?

Accepted Answer

Historical bias. Historical bias, because the model's lower scoring of female candidates stems directly from training data that reflects past hiring decisions where the company historically hired more men. This bias is embedded in the data itself, not introduced by the algorithm or data collection method. Historical bias occurs when the training data encodes societal or organizational prejudices from the past, which the model then perpetuates.

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Selection bias

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Algorithmic bias

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Confirmation bias

Question 5

What is the primary function of an AI ethics board within an organization?

Accepted Answer

Reviewing AI projects for ethical compliance. The primary function of an AI ethics board is to review AI projects for ethical compliance, ensuring that the organization's AI systems adhere to established ethical principles, legal standards, and governance frameworks. This board typically assesses risks related to bias, fairness, transparency, and accountability before deployment, rather than engaging in technical development or operational tasks.

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Developing algorithms

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Managing cloud infrastructure

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Marketing AI products

Question 6

A financial institution uses a deep learning model for loan approvals. Under the EU AI Act, this is considered a high-risk AI system. Which mandatory requirement must the institution fulfill before deployment?

Accepted Answer

Conduct a risk assessment and bias testing. Under the EU AI Act, high-risk AI systems must undergo a conformity assessment that includes a risk assessment and bias testing to ensure fairness, transparency, and non-discrimination before deployment. This requirement is mandated by Articles 9 and 10 of the Act, which specifically address risk management and data governance for high-risk systems. Option D correctly identifies this mandatory step, as the institution must demonstrate that the model does not produce biased outcomes that could lead to discriminatory lending practices.

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Obtain certification from an ISO 27001 auditor

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Publish the model's source code publicly

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Register the AI system with the national data protection authority

Question 7

A retail company wants to build a model to predict customer churn based on purchase history and demographics. The dataset includes categorical features like region and gender, and numerical features like total spend. What is the best initial step before training the model?

Accepted Answer

One-hot encode categorical variables and normalize numerical variables. One-hot encoding categorical variables and normalizing numerical variables is standard preprocessing to convert categorical data into numeric format and scale features, which many algorithms require for optimal performance.

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Train a deep neural network directly on raw data

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Remove all categorical features to simplify the model

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Perform principal component analysis (PCA) on all features

Question 8

An AI system uses a pre-trained image classification model to detect defects in manufacturing. The team wants to deploy the model in an edge device with limited GPU memory. Which technique should they consider first?

Accepted Answer

Apply quantization to reduce model size. Quantization reduces the precision of the model's weights and activations (e.g., from 32-bit floating point to 8-bit integer), which significantly shrinks the model size and memory footprint while often maintaining acceptable accuracy. This is the most direct and effective first step for deploying a pre-trained model on an edge device with limited GPU memory, as it requires no retraining and immediately addresses the memory constraint.

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Train the model from scratch using a smaller dataset

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Use a larger model with more parameters for higher accuracy

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Increase the batch size to improve throughput

Question 9

An ML team uses the model registry above. After deploying version 3 to production, they discover it has a critical bug. What is the fastest way to roll back to a stable version without re-deploying from scratch?

Accepted Answer

Redeploy version 1 by updating the production deployment to use its artifact. It directly updates the production deployment to reference the artifact of version 1, which is a stable, previously validated version. This avoids the overhead of re-deploying from scratch by simply pointing the existing deployment to a known good artifact in the model registry, leveraging the registry's artifact storage and deployment integration.

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Promote version 2 from Staging to Production

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Retrain the model with corrected data

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Delete version 3 from the registry

Question 10

An AI system is designed to automatically execute actions on behalf of users, such as sending emails. The security team is concerned about excessive agency. Which mitigation is most effective?

Accepted Answer

Restrict the functions the model can call and require human approval for sensitive actions. Limiting the scope of actions and requiring user confirmation for critical actions reduces the risk of the LLM performing unintended actions. This directly addresses excessive agency.

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Disable output filtering

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Increase the model's context window

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Use a larger model

Question 11

A financial institution uses a regression model to predict credit risk. The model has a high R-squared on training data but low R-squared on test data. Which of the following is the most likely cause?

Accepted Answer

The model is overfitting the training data.. A high R-squared on training data combined with a low R-squared on test data is the classic symptom of overfitting. The model has memorized noise and specific patterns in the training set rather than learning generalizable relationships, causing poor performance on unseen data.

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The features were not standardized before training.

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The model is underfitting the training data.

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There is multicollinearity among the input features.

Question 12

Refer to the exhibit. A data scientist defines a model configuration in JSON. Which component is missing from the configuration for a complete machine learning pipeline?

Accepted Answer

Data preprocessing steps. A complete machine learning pipeline must include data preprocessing steps to transform raw data into a format suitable for model training. The JSON configuration defines the model type, evaluation metrics, and training hyperparameters, but omits any specification for data cleaning, normalization, feature encoding, or splitting, which are essential for reproducibility and model performance.

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Training hyperparameters

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Model type

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Evaluation metrics

Question 13

A team is deploying a fine-tuned LLM for code generation. They need to ensure the model output is always valid JSON. Which prompt engineering technique should they use?

Accepted Answer

Few-shot examples of valid JSON outputs. Few-shot prompting provides the model with examples of valid JSON outputs, conditioning it to mimic the format in its response. This is an effective technique for constraining output structure without requiring model retraining or special modes.

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Chain-of-thought prompting

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Temperature setting to 0

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Using a larger model variant

Question 14

Which THREE components are essential for implementing a successful MLOps pipeline for a continuously deployed AI system?

Accepted Answer

Model registry for version control and metadata management. A model registry (C) is essential for MLOps because it provides version control, metadata management, and lineage tracking for all trained models. This enables reproducibility, auditability, and seamless rollback in a continuously deployed AI system, ensuring that only validated models are promoted to production.

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Manual approval gates for each deployment

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Canary deployment strategy

Question 15

A company is deploying a fraud detection model that must return predictions within 100ms to avoid transaction delays. The team is deciding between batch and real-time inference. Which factor most strongly supports a real-time inference architecture?

Accepted Answer

The application requires immediate feedback for each transaction. Real-time inference is required when the application must return predictions within strict latency bounds (e.g., 100ms) to avoid transaction delays. The need for immediate feedback per transaction directly aligns with a real-time architecture, where each request is processed individually as it arrives, rather than waiting for a batch window. Batch inference would introduce unacceptable latency because it processes groups of records on a schedule, not on-demand.

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The model requires large amounts of historical data for each prediction

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The infrastructure budget is limited and must be optimized

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The model can be retrained weekly using gathered data