AI0-001 Practice Test 2 — 30 Questions

Question 1

A company is choosing between fine-tuning and RAG for a legal document assistant. Which TWO factors would MOST strongly favor RAG over fine-tuning?

Accepted Answer

The legal documents are updated frequently (weekly). RAG allows dynamic retrieval from a changing document base without retraining, and provides citation sources for transparency — critical in regulated domains like law.

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The model needs to understand complex legal terminology

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The queries require deep reasoning across multiple documents

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The company has limited compute budget for training

Question 2

A credit union uses an AI model to approve personal loans. The model was trained on historical data from the past five years. A recent internal review shows that the model approves loans predominantly for white applicants compared to other ethnicities, even when income and credit scores are similar. The credit union wants to comply with fair lending laws without significantly reducing overall approval rates. The data science team has access to the training data. What is the most appropriate remediation step?

Accepted Answer

Resample the training data to ensure balanced representation of ethnicities. Resampling the training data to ensure balanced representation of ethnicities directly addresses the root cause of the bias—skewed historical data—without altering the model's decision logic or approval thresholds. By rebalancing the dataset (e.g., oversampling underrepresented groups or undersampling the majority), the model learns from a more equitable distribution of features, reducing disparate impact while preserving overall approval rates. This approach aligns with fair lending laws by mitigating bias at the data level, which is the most fundamental and effective remediation step.

Answer

Apply a fairness constraint that penalizes the model for disparate impact

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Discontinue the AI model and use manual approval for all loans

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Adjust the approval threshold so that approval rates are equal across ethnic groups

Question 3

A financial institution wants to deploy an AI system that automates loan approvals. Under the EU AI Act, this system would be classified as high-risk. Which of the following is a MANDATORY requirement for high-risk AI systems before market placement?

Accepted Answer

Establishing a risk management system throughout the system lifecycle. The EU AI Act requires a conformity assessment (often including third-party involvement) for high-risk AI systems to ensure compliance with requirements such as data governance, transparency, and human oversight. The other options are either voluntary, not required, or apply to lower-risk tiers.

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Obtaining a CE marking of conformity through self-assessment only

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Acquiring liability insurance for AI-related harms

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Publishing the training dataset in an open repository

Question 4

Which TWO techniques are commonly used to prevent overfitting in deep neural networks?

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 and effectively training an ensemble of sub-networks.

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Using a larger learning rate

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L1 regularization

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Increasing the number of layers

Question 5

A company wants to generate realistic images of new product designs. They have a large dataset of existing product images. Which generative AI approach is MOST suitable for creating novel, high-quality images?

Accepted Answer

Generative adversarial network (GAN). GANs (Generative Adversarial Networks) consist of a generator and discriminator that compete, producing highly realistic images. Diffusion models are also good but GANs are historically the go-to for image generation. VAEs produce blurrier images; LLMs are for text.

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Large language model (LLM)

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Variational autoencoder (VAE)

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Diffusion model

Question 6

A large enterprise is developing an internal LLM-powered assistant that can access the internet and execute code. To mitigate risks from excessive agency (e.g., the model performing unauthorized actions), which THREE security measures should be implemented?

Accepted Answer

Require human-in-the-loop approval for code execution and write operations. Requiring human-in-the-loop approval for code execution and write operations directly enforces a control over the model's agency, preventing it from performing unauthorized actions such as modifying files or executing arbitrary commands. This measure ensures that any action with side effects is vetted by a human operator, mitigating the risk of excessive agency where the LLM could autonomously cause harm.

Answer

Deploy monitoring for anomalous input patterns

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Apply output filtering to block sensitive data in responses

Question 7

Under the GDPR, individuals have the right to not be subject to a decision based solely on automated processing if it produces legal effects. Which of the following is a typical safeguard that organisations must provide to comply with this right?

Accepted Answer

The right to obtain human intervention on the part of the controller. GDPR requires human intervention as a safeguard for automated decisions with legal effects. The right to explanation and human oversight are key. Deleting data is not a safeguard; consent is not sufficient; detailed explanations are part of transparency.

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The right to have all personal data deleted immediately

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The right to receive a detailed mathematical explanation of the model

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The right to demand a more favourable automated decision

Question 8

A team is building a regression model to predict house prices. The dataset includes numerical features (square footage, number of bedrooms) and categorical features (neighborhood, roof type). The categorical features have high cardinality (neighborhood has 200+ unique values). Which encoding strategy should the team use to avoid overfitting and maintain model interpretability?

Accepted Answer

Target encoding with regularization.. Target encoding with regularization is the best choice because it replaces each categorical value with the mean of the target variable for that category, which captures the relationship between the category and house prices. Regularization (e.g., adding a prior or using cross-validation) shrinks the encoded values toward the global mean, preventing overfitting on rare categories (e.g., neighborhoods with only a few houses). This maintains interpretability because each encoded value directly reflects the average price impact of that category, unlike black-box embeddings.

Answer

Label encoding.

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Binary encoding.

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One-hot encoding with feature selection.

Question 9

Which TWO statements correctly describe the difference between supervised and unsupervised learning?

Accepted Answer

Supervised learning requires labeled data. Supervised learning relies on labeled datasets where each training example is paired with an output label, enabling the model to learn a mapping from inputs to outputs. This is a fundamental distinction from unsupervised learning, which works with unlabeled data to find inherent structures or patterns.

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Supervised learning is only used for classification

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Unsupervised learning always requires a target variable

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Supervised learning is a subset of reinforcement learning

Question 10

A machine learning engineer is training a neural network for image classification. The training loss decreases slowly and the model accuracy improves only marginally each epoch. Which hyperparameter adjustment is MOST likely to accelerate convergence?

Accepted Answer

Increase the learning rate. The training loss decreasing slowly and accuracy improving marginally each epoch indicates that the learning rate is too small, causing the optimizer to take very small steps toward the minimum of the loss function. Increasing the learning rate allows the optimizer to take larger steps per update, which accelerates convergence. Option C is correct because adjusting the learning rate directly addresses the step size in gradient descent.

Answer

Add more hidden layers

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Increase the batch size

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Decrease the number of epochs

Question 11

A startup is building a chatbot for customer service. They have 500 recorded conversations and want to use a pre-trained language model to generate responses. However, they have limited computational resources and need the chatbot to respond in real-time. They are considering fine-tuning a large model like GPT-3 or using a smaller model like DistilBERT. The conversation data contains industry-specific jargon. Which approach should they take?

Accepted Answer

Fine-tune DistilBERT on the conversation data. Fine-tuning DistilBERT on the 500 recorded conversations allows the model to adapt to industry-specific jargon while maintaining real-time responsiveness due to its smaller size. DistilBERT is a distilled version of BERT that retains 97% of BERT’s language understanding with 40% fewer parameters, making it suitable for limited computational resources. Fine-tuning on domain-specific data is essential here, as pre-trained models like GPT-3 lack exposure to the startup’s specialized terminology, and using a smaller model ensures low-latency inference for real-time chatbot responses.

Answer

Use GPT-3 via API without fine-tuning

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Train a custom RNN from scratch on the conversations

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Implement a rule-based system with keywords

Question 12

An attacker repeatedly queries a public LLM API with carefully crafted inputs to reconstruct the model's architecture and approximate weights. This is an example of which attack?

Accepted Answer

Model extraction. Model extraction attacks involve querying a public API with carefully crafted inputs to reconstruct a target model's architecture and approximate weights. By analyzing the outputs (e.g., logits or probabilities), an attacker can train a substitute model that mimics the original, enabling offline exploitation or competitive intelligence. This directly matches the scenario described.

Answer

Data poisoning

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Membership inference

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Model inversion

Question 13

An organization wants to integrate an AI-powered summarization feature into their existing web application. The AI service will be called via API. Which factor is MOST important to consider for cost management?

Accepted Answer

Token pricing of the AI model. Token pricing directly impacts cost because API calls are billed based on the number of tokens (input + output). Understanding token usage helps estimate and control expenses.

Answer

Authentication method (API key vs. OAuth)

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Rate limits per minute

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Network latency to the API endpoint

Question 14

A healthcare company is developing a predictive model to identify patients at risk of readmission within 30 days. The data engineering team has built a pipeline that collects data from multiple sources, including electronic health records (EHR), lab results, and wearable device data. During initial testing, the model's performance is poor, with high false positives. Upon investigation, the team discovers that the data contains significant temporal misalignment: lab results are timestamped when ordered, not when collected; wearable data is aggregated hourly; and EHR data has inconsistent update frequencies. The data pipeline currently joins all features on the patient ID without aligning timestamps. The data volume is large, and processing time is a concern. Which action should the data engineering team take to most effectively address the issue and improve model performance?

Accepted Answer

Implement a window-based feature aggregation (e.g., 6-hour windows) and align all features to the same time windows before joining.. Implementing a window-based feature aggregation with consistent time windows (e.g., 6-hour or 12-hour) and aligning all data to those windows before joining ensures temporal consistency and reduces noise. This approach addresses the root cause of misalignment while managing data volume through aggregation. Simply discarding data or padding with zeros loses valuable information. Using an interpolation algorithm may introduce unrealistic values for irregularly sampled data. Leaving the pipeline as-is and tuning the model does not fix the data quality issue.

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Discard all records where timestamps do not match exactly across sources, and only use records with perfect alignment.

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Leave the pipeline unchanged and instead adjust the model's classification threshold to reduce false positives.

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Use a data imputation algorithm to fill in missing timestamps and then join on the nearest timestamp.

Question 15

A company is implementing an AI solution for fraud detection. The dataset is highly imbalanced (only 1% fraudulent transactions). Which THREE techniques are most appropriate to address class imbalance? (Select three.)

Accepted Answer

Apply cost-sensitive learning by assigning a higher misclassification cost to the minority class.. Cost-sensitive learning directly addresses class imbalance by assigning a higher misclassification cost to the minority class (fraudulent transactions). This forces the model to penalize false negatives more heavily, thereby improving recall for the minority class without altering the dataset distribution.

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Reduce the number of features using principal component analysis (PCA).

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Use accuracy as the primary evaluation metric.

Question 16

A healthcare AI system is subject to GDPR because it processes patient data. Which THREE requirements must the system satisfy?

Accepted Answer

Right to explanation of decisions. Article 22 of the GDPR grants data subjects the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects or similarly significant effects. For healthcare AI systems, this means patients have the right to obtain an explanation of the decision reached by the algorithm, such as how a diagnosis or treatment recommendation was derived. This requirement ensures transparency and accountability in high-stakes automated decisions.

Answer

Explicit consent from all data subjects

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Data retention period of at least 10 years

Question 17

A team is using a pre-trained language model for sentiment analysis. They want to adapt it to a specific domain with limited labeled data. Which approach is most efficient?

Accepted Answer

Fine-tune the pre-trained model on domain data. Fine-tuning a pre-trained language model on domain-specific labeled data is the most efficient approach because it leverages the general language understanding learned from large corpora while adapting to the target domain with minimal additional data. This process uses transfer learning, where only the final layers or a subset of parameters are updated, significantly reducing the amount of labeled data and compute required compared to training from scratch.

Answer

Use the pre-trained model as is

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Train a new model from scratch

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Ensemble multiple pre-trained models

Question 18

A company has developed a deep learning model for image classification. The team wants to deploy the model to production with high availability and scalability. Which approach should they use?

Accepted Answer

Use a containerized approach with Kubernetes.. Containerization with Kubernetes provides the orchestration, auto-scaling, and self-healing capabilities required for high availability and scalability in production. Kubernetes manages container lifecycles, distributes traffic across replicas via Services and Ingress controllers, and can automatically scale pods based on CPU/memory metrics or custom metrics, ensuring the deep learning model handles variable loads without downtime.

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Run the model on a laptop during business hours.

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Deploy the model as a monolithic application on a single server.

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Embed the model directly into a mobile app.

Question 19

A healthcare AI startup has developed a model to detect diabetic retinopathy from retinal images. The model achieved 96% sensitivity and 94% specificity on a validation set from the same distribution as the training data. After deployment in a rural clinic, the model's sensitivity drops to 80%. The data team analyzes the clinical images from the clinic and finds that the images have lower resolution and different lighting conditions compared to the training dataset. The team has the ability to collect more data from the clinic and retrain the model. What is the BEST course of action?

Accepted Answer

Collect additional retinal images from the rural clinic, label them, and retrain the model including the new data.. The performance drop is caused by a domain shift (lower resolution, different lighting) between the training and deployment data. The most direct and effective solution is to collect labeled images from the target domain (rural clinic) and retrain the model, which aligns with the principle of domain adaptation through data augmentation. This approach addresses the root cause by exposing the model to the actual distribution it will encounter in production.

Answer

Reduce the model's complexity by removing several convolutional layers to improve generalization.

Answer

Apply transfer learning using a model pre-trained on a different medical imaging dataset.

Answer

Implement adversarial validation to identify which images are out-of-distribution and filter them out.

Question 20

Which hardware accelerator is specifically designed by Google for training and inference of machine learning models, particularly their TensorFlow framework?

Accepted Answer

TPU. TPU (Tensor Processing Unit) is Google's custom ASIC designed to accelerate ML workloads, especially with TensorFlow.

Answer

NPU

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FPGA

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GPU