AI0-001 Practice Test 22 — 15 Questions

Question 1

Refer to the exhibit. A data scientist observes the training output. Which issue is most likely?

Accepted Answer

Overfitting. The exhibit shows training loss decreasing while validation loss increases after a certain epoch, which is the classic signature of overfitting. The model is memorizing the training data rather than learning generalizable patterns, leading to poor performance on unseen data.

Answer

Underfitting

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Data augmentation failure

Answer

Model compression

Question 2

A financial firm deploys an LLM for automated trading advice. To prevent over-reliance, which combination of guardrails should be implemented? (Assume multiple options but choose the MOST comprehensive single approach.)

Accepted Answer

Output filtering and content moderation. Output filtering and content moderation directly address over-reliance by ensuring the LLM's trading advice includes disclaimers, risk warnings, and confidence levels, and by blocking overly assertive or misleading outputs. This combination prevents users from blindly trusting the model, which is critical in high-stakes financial environments where automated advice must be treated as a decision-support tool, not a definitive source.

Answer

Red teaming the model

Answer

Rate limiting and input validation

Answer

Differential privacy

Question 3

Based on the exhibit, what is the most likely issue with the model training?

Accepted Answer

Overfitting. The exhibit shows training loss decreasing while validation loss increases after a certain point, which is a classic sign of overfitting. The model is memorizing the training data rather than generalizing, leading to poor performance on unseen validation data.

Answer

Vanishing gradient

Answer

Learning rate too high

Answer

Underfitting

Question 4

A machine learning engineer wants to prevent data poisoning during the training of a model. Which practice is MOST effective for ensuring the integrity of the training data?

Accepted Answer

Secure data pipelines. Secure data pipelines include validation, checksums, and access controls to ensure data integrity. Output filtering is for outputs, red teaming tests the model, and differential privacy adds noise but does not prevent poisoning.

Answer

Differential privacy

Answer

Red teaming the model

Answer

Output filtering

Question 5

An AI system is deployed to detect fraudulent transactions. The system flags 5% of transactions as fraudulent, but the actual fraud rate is 0.1%. The business sees many false positives and wants to reduce them without significantly increasing false negatives. Which metric should be prioritized for optimization?

Accepted Answer

F1 score. The F1 score balances precision and recall, making it ideal when false positives are costly but false negatives must not increase significantly. Optimizing precision alone would reduce false positives but could increase false negatives, while recall alone would not address the false positive problem. The F1 score ensures both metrics are jointly optimized, aligning with the business requirement.

Answer

Recall

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Accuracy

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Precision

Question 6

A company deploys an LLM chatbot that has access to a database of customer orders. They want to prevent the LLM from revealing order details unless the user is authenticated as the owner. Which security control should be implemented?

Accepted Answer

Access controls on the model and API. Access controls on the model and API (Option D) are the correct security control because they enforce authentication and authorization at the API gateway or model endpoint level, ensuring that only the authenticated owner can query their own order details. This prevents unauthorized users from invoking the LLM to retrieve sensitive data, regardless of the prompt content. Without such access controls, the LLM would have no inherent mechanism to verify user identity before processing requests.

Answer

Output filtering

Answer

Rate limiting

Answer

Input validation and sanitization

Question 7

Which TWO are best practices for versioning machine learning models? (Choose 2)

Accepted Answer

Tag each model with training date, hyperparameters, and performance metrics. Tagging each model with training date, hyperparameters, and performance metrics creates a reproducible audit trail. This practice aligns with MLOps principles, enabling teams to trace model behavior back to specific training runs and compare versions objectively.

Answer

Use the same model version for all deployments

Answer

Store only the final model binary without metadata

Answer

Manually rename model files with version numbers

Question 8

A team deploys a machine learning model as a REST API. They want to monitor model drift. Which metric is MOST appropriate for detecting drift in the input data distribution?

Accepted Answer

Population stability index (PSI) comparing training and recent data.. Population stability index (PSI) is the most appropriate metric for detecting drift in input data distribution because it directly measures the shift between the training data distribution and the recent production data distribution. PSI is calculated by binning both distributions and computing the sum of (proportion in bin of recent data minus proportion in bin of training data) times the natural log of their ratio, making it sensitive to changes in feature distributions without requiring ground truth labels.

Answer

Model accuracy on a recent holdout set.

Answer

F1 score on the training data.

Answer

Root mean squared error (RMSE) on test data.

Question 9

A model serving pod is failing with OOMKilled. What is the most likely cause?

Accepted Answer

The model requires more memory than the 2Gi limit. An OOMKilled error in Kubernetes indicates that a container exceeded its memory limit and was terminated by the Out Of Memory (OOM) killer. The most common cause is that the model's inference or training workload requires more memory than the configured resource limit (e.g., 2Gi), forcing the kernel to kill the process. This is a direct result of the container's memory request/limit mismatch with the actual consumption.

Answer

The container image is corrupted

Answer

The model version is outdated

Answer

The Kubernetes cluster has run out of disk space

Question 10

A company must deploy a new model version with zero downtime. The current model is served via a REST API on a Kubernetes cluster. Which deployment strategy should the team use to gradually shift traffic to the new version while monitoring for errors?

Accepted Answer

Canary deployment. A canary deployment gradually shifts a small percentage of traffic to the new model version while the majority continues to hit the stable version. This allows the team to monitor for errors and roll back quickly if issues arise, achieving zero downtime. It is the ideal strategy for validating a new model in production with minimal risk.

Answer

Blue-green deployment

Answer

Recreate deployment

Answer

Rolling update

Question 11

An AI practitioner needs to extract key phrases from a large collection of customer support emails for trend analysis. Which technique is MOST suitable?

Accepted Answer

Named entity recognition (NER). Named entity recognition (NER) is the most suitable technique because it automatically identifies and extracts predefined entities such as names, dates, product names, and other key phrases from text. This directly supports extracting key phrases from customer support emails for trend analysis. Other techniques like language translation, text classification, and sentiment analysis do not focus on extracting specific phrases.

Answer

Language translation

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Text classification

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Sentiment analysis

Question 12

A company is training a large language model and wants to reduce its carbon footprint. Which practice is MOST effective for reducing training energy consumption while maintaining model quality?

Accepted Answer

Use mixed-precision training and prune unnecessary parameters. Green AI practices include using more efficient hardware (like GPUs with lower power draw), model pruning, and early stopping. Using CPUs is slower and less efficient. Increasing batch size without tuning can hurt convergence. Using a larger model increases energy. Training on renewable energy reduces the carbon impact but does not reduce energy consumption itself.

Answer

Increase the batch size to the maximum the GPU memory allows

Answer

Use a larger model architecture to achieve higher accuracy faster

Answer

Train the model on CPUs instead of GPUs

Question 13

A machine learning engineer is deploying a real-time anomaly detection system for manufacturing sensor data. The system must process thousands of readings per second with minimal latency. Which deployment architecture is BEST suited?

Accepted Answer

AI microservices with an async processing queue and streaming responses. AI microservices with async processing queues and streaming responses can handle high throughput and low latency for real-time data.

Answer

Batch processing using Apache Spark jobs triggered hourly

Answer

Serverless functions deployed on a CDN

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A monolithic web application with a relational database

Question 14

Which TWO actions are most appropriate for managing model drift in a production AI system?

Accepted Answer

Periodically retrain the model on recent data. Periodically retraining the model on recent data is a fundamental strategy to combat model drift, ensuring the model adapts to changes in the underlying data distribution (e.g., concept drift or covariate shift). This aligns with MLOps best practices for maintaining model accuracy over time in production AI systems.

Answer

Freeze the model to prevent any changes

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Roll back to a previous model version if performance degrades

Answer

Manually review all model predictions

Question 15

A chatbot developer uses a transformer-based model for customer service. Users complain that the chatbot sometimes gives offensive responses. Which technique should be applied first to mitigate this issue?

Accepted Answer

Review and filter the training dataset for offensive or biased language, then fine-tune the model.. The root cause of offensive responses in transformer-based models is typically biased or toxic language present in the training data. Reviewing and filtering the dataset to remove such content, followed by fine-tuning the model, directly addresses the source of the problem. This approach aligns with the principle of data-centric AI, where improving data quality is the first step before modifying model architecture or inference parameters.

Answer

Increase the model size to improve its understanding of context.

Answer

Decrease the temperature parameter to make outputs more deterministic.

Answer

Train a separate classifier to detect offensive outputs in real time.