AI0-001 Practice Test 32 — 15 Questions

Question 1

A data scientist is preparing a dataset for a supervised learning model. The dataset contains missing values in 15% of the rows for a numeric feature. Which preprocessing technique should be applied to minimize bias?

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

Accepted Answer

Impute missing values with the mean of the feature.. Imputing missing values with the mean of the feature is the correct approach because it preserves the overall distribution of the numeric feature and avoids introducing systematic bias when only 15% of rows are affected. Removing rows would reduce sample size and potentially introduce selection bias, while encoding missing values as a separate category is inappropriate for numeric features in regression or classification tasks. Mean imputation is a standard, low-bias technique for MCAR (Missing Completely At Random) data in supervised learning pipelines.

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Remove all rows with missing values.

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Encode the missing values as a separate category.

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Use a model that handles missing values natively.

Question 2

A data scientist splits a dataset into training (80%) and test (20%). After training, the model achieves 95% accuracy on training and 60% on test. Which step should the data scientist take first?

Accepted Answer

Apply regularization. The model shows high training accuracy (95%) but significantly lower test accuracy (60%), which is a classic sign of overfitting. Regularization (Option C) directly addresses overfitting by adding a penalty term to the loss function (e.g., L1 or L2 regularization), discouraging the model from learning overly complex patterns that do not generalize. This is the first step because it targets the core issue without requiring additional data or increasing complexity.

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Collect more data

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Use cross-validation

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Increase model complexity

Question 3

An ML engineer wants to deploy a model as a REST API that can scale to handle thousands of inference requests per second. Which serving approach is most appropriate?

Accepted Answer

Deploy the model as a REST API endpoint using a containerized inference server. Deploying the model as a REST API endpoint using a containerized inference server (e.g., TensorFlow Serving, TorchServe, or NVIDIA Triton Inference Server) is the most appropriate approach for handling thousands of inference requests per second. These servers are designed for high-throughput, low-latency serving, support horizontal scaling via load balancers, and provide built-in batching and model versioning. REST APIs are stateless and can be easily integrated with existing web infrastructure, making them ideal for production-scale inference.

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Export the model to ONNX format and use a batch processing pipeline

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Use gRPC streaming for all inference requests

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Run the model directly on the client device

Question 4

A financial institution deploys an AI model for loan approval. To meet regulatory requirements under the EU AI Act for high-risk AI systems, they must ensure human oversight. Which implementation best satisfies the requirement for meaningful human intervention?

Accepted Answer

Provide a human reviewer with the ability to override the model's decision before finalization. Human-in-the-loop oversight with an override mechanism allows a qualified human to review and override automated decisions, satisfying the EU AI Act's requirement for high-risk systems.

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Audit model decisions quarterly for bias

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Allow users to appeal decisions through a customer service hotline

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Display a confidence score for each decision

Question 5

A security analyst is reviewing logs from an AI-powered recommendation system and notices an unusually high number of requests for products from a specific vendor. The analyst suspects data poisoning. Which mitigation strategy should be implemented first?

Accepted Answer

Implement input validation and sanitization for training data. Input validation and sanitization directly prevent malicious or anomalous data from entering the training pipeline, which is the root cause of data poisoning. In an AI-powered recommendation system, poisoned training data can cause the model to learn biased associations, such as favoring a specific vendor. By validating and sanitizing inputs before they are used for training, the attack vector is blocked at the earliest stage, making it the most effective first mitigation step.

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Encrypt all training data at rest

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Deploy an anomaly detection system on model outputs

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Retrain the model with a smaller, curated dataset

Question 6

An AI engineer is designing a system to detect unusual patterns in network traffic that may indicate a security breach. The system should learn from normal traffic patterns and flag deviations. Which machine learning approach is MOST appropriate?

Accepted Answer

Unsupervised anomaly detection. Unsupervised anomaly detection is the most appropriate approach because the system must learn 'normal' traffic patterns from unlabeled data and then flag deviations without requiring pre-labeled examples of attacks. This aligns with the core requirement of detecting unknown or novel security breaches, which supervised methods cannot handle due to the lack of labeled attack data.

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Reinforcement learning with reward shaping

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Supervised classification using logistic regression

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Semi-supervised learning with a small labeled set

Question 7

A machine learning engineer is training a transformer model for machine translation. The model's perplexity on the validation set is 8.5, and the BLEU score is 32. After increasing the number of encoder layers from 6 to 12, perplexity drops to 7.2 but BLEU decreases to 28. What is the MOST likely cause?

Accepted Answer

The model is overfitting the training data. Perplexity measures language model confidence, but BLEU measures translation quality. The deeper model may overfit to the training data, reducing perplexity but hurting generalization to validation translations. Overfitting causes high confidence (low perplexity) but poor translation diversity or exact matches.

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The batch size is too small

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The model is underfitting the training data

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The learning rate is too high

Question 8

When implementing a vector store for a RAG system, which similarity search metric is MOST commonly used to find the most relevant document chunks for a given query embedding?

Accepted Answer

Cosine similarity. Cosine similarity is the most common metric for comparing embedding vectors in RAG because it measures the angle between vectors, which works well for high-dimensional semantic embeddings.

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Manhattan distance

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Euclidean distance

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Dot product

Question 9

A data scientist is training a binary classification model to detect fraudulent transactions. The dataset has 99% legitimate transactions and 1% fraudulent. The model achieves 99% accuracy but fails to catch most fraud. Which metric should the team prioritize to evaluate model performance?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positive cases (fraud) correctly identified. With 99% accuracy but failing to catch most fraud, the model is biased toward the majority class (legitimate transactions), so recall is the critical metric to ensure fraud detection improves.

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F1 score

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Precision

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Accuracy

Question 10

A team is designing a deep learning pipeline for a computer vision task. They want to reduce overfitting. Which two techniques are specifically effective for this purpose? (Select TWO.)

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 that improves generalization.

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Using a smaller batch size

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Adding more layers

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Increasing the learning rate

Question 11

An AI risk manager is applying the NIST AI Risk Management Framework (AI RMF). In which function would the organization establish a risk management process and assign roles and responsibilities for AI oversight?

Accepted Answer

Govern. The Govern function in the NIST AI RMF is specifically designed to establish organizational structures, policies, and accountability mechanisms for AI risk management. This includes defining roles and responsibilities, setting risk management processes, and ensuring oversight across the AI lifecycle. The other functions (Map, Measure, Manage) focus on different aspects such as understanding context, assessing risks, and treating risks, respectively.

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Map

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Manage

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Measure

Question 12

When evaluating a binary classification model, which two metrics are most appropriate for imbalanced datasets? (Choose two.)

Accepted Answer

Recall. Recall (Option C) is correct because it measures the proportion of actual positive cases correctly identified, which is critical in imbalanced datasets where the minority class is of primary interest. Precision (Option E) is correct because it measures the accuracy of positive predictions, helping to avoid false positives when the positive class is rare. Together, recall and precision provide a balanced view of model performance on the minority class, unlike accuracy which can be misleadingly high by simply predicting the majority class.

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Accuracy

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Mean absolute error

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R-squared

Question 13

A data scientist trains a deep learning model on a large dataset. The training loss decreases steadily but the validation loss starts increasing after 20 epochs. The scientist uses early stopping with patience=5. Which of the following is the MOST likely cause and best corrective action?

Accepted Answer

Model is overfitting; add dropout regularization.. The training loss decreasing while validation loss increasing after 20 epochs is a classic sign of overfitting, where the model memorizes training data noise instead of generalizing. Early stopping with patience=5 would halt training after 5 epochs of no validation improvement, but the root cause is overfitting. Adding dropout regularization randomly drops neurons during training, forcing the network to learn more robust features and reducing overfitting.

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Training data is not representative; collect more data.

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Model is underfitting; increase model capacity.

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Learning rate too high; reduce learning rate.

Question 14

A deep learning model for image classification is overfitting the training data. The team has already tried data augmentation and dropout. Which additional technique should they implement to reduce overfitting?

Accepted Answer

Early stopping. Early stopping (Option D) is the correct additional technique because it halts training when validation performance stops improving, directly preventing the model from memorizing noise in the training data. Since data augmentation and dropout are already in use, early stopping provides a complementary regularization effect by limiting the number of training iterations before overfitting occurs.

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Batch normalization

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Increase number of epochs

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Gradient clipping

Question 15

A developer is using Hugging Face Transformers to fine-tune a BERT model for sentiment analysis. They want to track experiments, log metrics, and compare runs. Which MLOps tool should they integrate?

Accepted Answer

MLflow. MLflow is the correct choice because it is purpose-built for experiment tracking, metric logging, and run comparison in machine learning workflows. It provides an API to log parameters, metrics, and artifacts, and its UI allows easy comparison of different fine-tuning runs, which directly matches the developer's need to track experiments and compare runs for a BERT sentiment analysis model.

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Apache Airflow

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Docker

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Kubeflow