AI0-001 Practice Exam 3 — 20 Questions

Question 1

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?

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

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 2

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 3

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 4

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.

Answer

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 5

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.

Answer

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

Answer

Use accuracy as the primary evaluation metric.

Question 6

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 7

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 8

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.

Answer

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 9

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.

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

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

Question 10

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

Question 11

Refer to the exhibit. An auditor reports that the model's fairness check was bypassed in a recent deployment. Based on the policy, what is the most likely cause?

Accepted Answer

Data scientist role has deploy permission, allowing deployment without fairness validation. (Data scientist role has deploy permission, allowing deployment without fairness validation) is correct. The policy shows fairness_check required, but if the deployment process does not enforce it, the data scientist could bypass it. Option A (Auditor lacks evaluate) is unrelated to deployment. Option C (Fairness threshold low) does not cause bypass. Option D (External user inference) is unrelated.

Answer

The auditor role lacks 'evaluate' permission

Answer

Fairness check threshold is set to 0.8, which is too low

Answer

External_user role can perform inference, which triggers unfair predictions

Question 12

Which neural network architecture is specifically designed to handle sequential data and mitigate the vanishing gradient problem?

Accepted Answer

Long Short-Term Memory (LSTM) network. LSTM (Long Short-Term Memory) is a type of RNN designed with gating mechanisms to prevent vanishing gradients in long sequences. CNNs are for spatial data; vanilla RNNs suffer from vanishing gradients; transformers use attention but are not specifically designed to mitigate vanishing gradients (they use residual connections).

Answer

Convolutional Neural Network (CNN)

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Transformer

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Vanilla Recurrent Neural Network (RNN)

Question 13

An MLOps team automates model deployment with a CI/CD pipeline. A performance regression is detected after deploying a new model version. The team needs to automatically roll back to the previous version. Which approach best enables safe automated rollback?

Accepted Answer

Use a blue/green deployment with automated health checks and traffic switching. Blue/green deployment with automated health checks and traffic switching is the best approach because it allows the team to instantly route all traffic back to the previous (green) environment if the new (blue) version fails health checks. This ensures zero-downtime rollback without manual intervention, directly addressing the need for safe automated rollback in a CI/CD pipeline.

Answer

Maintain a manual rollback script that the operations team can run

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Deploy new models as canary releases and monitor for 24 hours

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Automatically keep the previous model version in storage for later use

Question 14

Refer to the exhibit. A team created an access policy for a fraud detection model endpoint. An intern reports being unable to access the model for testing. Reviewing the policy, what is the most likely cause?

Accepted Answer

The intern's role is explicitly denied in the policy. The exhibit shows an explicit `Deny` effect for the intern's role in the policy. In AWS IAM (or similar cloud provider) access policies, an explicit deny overrides any allow, so even if the intern's role is listed in allowed roles, the explicit deny will block access. This is a fundamental principle of IAM policy evaluation logic.

Answer

The intern's role is not included in the allowed roles

Answer

The policy JSON has a syntax error

Answer

The endpoint path is incorrect

Question 15

An operations team monitors a classification model in production. The confusion matrix for the model shows the following values: TP=1500, FN=500, FP=600, TN=2400. Which metric should the team calculate to assess the model's ability to avoid false positives?

Accepted Answer

Precision. Precision (TP / (TP + FP)) directly measures the proportion of positive identifications that were actually correct, making it the ideal metric to assess the model's ability to avoid false positives. With TP=1500 and FP=600, precision is 1500/(1500+600)=0.714, indicating that 71.4% of predicted positives are true positives, while 28.6% are false positives. The question explicitly asks about avoiding false positives, which is the inverse of precision's focus on the correctness of positive predictions.

Answer

Accuracy

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F1-score

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Recall

Question 16

A company deploys an LLM-based chatbot that retrieves data from external databases. An attacker embeds malicious instructions in a database record. When the chatbot retrieves that record, it executes the instructions, overriding its system prompt. Which type of attack is this?

Accepted Answer

Indirect prompt injection. This is an indirect prompt injection attack because the malicious instructions are embedded in a third-party data source (the database record) rather than being sent directly by the user. When the LLM retrieves and processes that record, the injected instructions override the system prompt, causing the chatbot to behave contrary to its intended design.

Answer

Model inversion attack

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Direct prompt injection

Answer

Membership inference attack

Question 17

A data scientist is building a classification model to detect fraudulent transactions. The dataset is highly imbalanced with only 1% fraudulent cases. Which approach should the scientist use to evaluate model performance most effectively?

Accepted Answer

F1 score. In highly imbalanced datasets like fraud detection (1% positive class), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy yet fail to detect any fraud. The F1 score (harmonic mean of precision and recall) is the most effective metric because it balances both false positives and false negatives, providing a single score that reflects the model's ability to correctly identify the minority class without being skewed by class imbalance.

Answer

Accuracy

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Recall

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Precision

Question 18

An AI system used for hiring has been found to exhibit racial bias against certain candidates. Which step should the organization take to mitigate this?

Accepted Answer

Regularly audit model predictions across demographic groups and retrain with fairness constraints.. Bias in AI systems is often embedded in training data or model behavior, not just in feature selection. Regularly auditing predictions across demographic groups and retraining with fairness constraints (e.g., demographic parity or equalized odds) allows the organization to detect and correct disparate impact without sacrificing model performance. This aligns with the AI0-001 focus on continuous monitoring and iterative improvement in AI operations.

Answer

Remove all demographic features from the model.

Answer

Use a different algorithm that is inherently unbiased.

Answer

Hire more diverse data scientists.

Question 19

An e-commerce company deploys a recommendation system using collaborative filtering. After launch, the system shows high accuracy for popular items but fails to recommend niche products to users who would likely buy them. Which technique should the team implement to improve recommendations for long-tail items?

Accepted Answer

Switch to a hybrid filtering approach that incorporates item metadata. Collaborative filtering relies on user-item interactions, which are sparse for niche products (the long tail). A hybrid filtering approach that incorporates item metadata (e.g., category, description, attributes) can bridge the gap by using content-based signals to recommend niche items even when interaction data is limited. This directly addresses the cold-start and sparsity problems for long-tail items.

Answer

Apply matrix factorization with higher latent factors

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Increase the weight of popular items in the recommendation score

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Collect more user interaction data over time

Question 20

Which THREE of the following are key components of an AI governance framework?

Accepted Answer

Regular auditing and monitoring for compliance.. Regular auditing and monitoring for compliance (A) is a key component of an AI governance framework because it ensures that AI systems operate within legal, ethical, and organizational policies over time. Continuous monitoring detects drift, bias, or security violations, while audits provide evidence of adherence to standards such as ISO/IEC 42001 or internal governance rules. Without this, governance becomes a static policy with no enforcement or verification.

Answer

Cloud-based deployment for scalability.

Answer

Model accuracy thresholds for production deployment.