AI0-001 Practice Test 2 — 25 Questions

Question 1

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. Option D is correct because 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 2

A dataset for a binary classification problem has 95% of samples in class "0" and 5% in class "1". The data scientist trains a logistic regression model and achieves 95% accuracy. Which metric should the scientist primarily use to evaluate model performance?

Accepted Answer

Precision, recall, and F1-score.. In a highly imbalanced dataset (95% class 0, 5% class 1), accuracy is misleading because a model can achieve 95% accuracy by simply predicting the majority class for all samples. Precision, recall, and F1-score provide a more nuanced view of performance on the minority class, which is typically the class of interest in binary classification problems. The F1-score, in particular, balances precision and recall, making it the primary metric for evaluating model effectiveness on imbalanced data.

Answer

R-squared.

Answer

Accuracy.

Answer

Mean squared error.

Question 3

A data scientist is evaluating a binary classification model for fraud detection. The dataset is highly imbalanced (99% non-fraud, 1% fraud). Which TWO metrics are most appropriate for assessing model performance? (Choose two.)

Accepted Answer

Precision. Precision is appropriate because it measures the proportion of predicted fraud cases that are actually fraudulent, which is critical when false positives (flagging legitimate transactions as fraud) are costly. In a highly imbalanced dataset like this (99% non-fraud), precision directly evaluates the model's ability to avoid overwhelming fraud analysts with false alarms.

Answer

F1 score

Answer

Area under the ROC curve (AUC-ROC)

Answer

Accuracy

Question 4

A data engineer is building a pipeline to ingest streaming data from IoT sensors. Which data storage solution is best suited for real-time analytics on timestamped sensor readings?

Accepted Answer

Time-series database. Time-series databases (TSDBs) are optimized for high-ingest rates of timestamped data and provide efficient downsampling, retention policies, and time-based aggregation functions. For IoT sensor streaming, a TSDB like InfluxDB or TimescaleDB delivers sub-second query performance on time-range scans, which is essential for real-time analytics.

Answer

Data warehouse

Answer

Relational database

Answer

Data lake

Question 5

While training a deep neural network, the loss function fails to converge and oscillates wildly. Which adjustment is most likely to stabilize training?

Accepted Answer

Reduce the learning rate. When the loss function oscillates wildly and fails to converge, it typically indicates that the learning rate is too high, causing the optimizer to overshoot the minima. Reducing the learning rate allows the gradient descent updates to take smaller, more stable steps, which helps the loss converge smoothly. This is a fundamental hyperparameter tuning step in deep learning training.

Answer

Increase the number of hidden layers

Answer

Decrease the batch size

Answer

Use a test set

Question 6

A data engineer needs to store training data in a format that supports columnar pruning during model training. Which storage format should they use?

Accepted Answer

Parquet. Parquet is the correct choice because it is a columnar storage format that enables column pruning, allowing the training process to read only the columns needed for model training rather than entire rows. This reduces I/O and speeds up data loading, which is critical for large-scale AI/ML workloads. Unlike row-oriented formats, Parquet stores data by columns, making it efficient for analytical queries and feature selection.

Answer

XML

Answer

JSON

Answer

CSV

Question 7

Which TWO of the following are common methods for mitigating bias in AI models?

Accepted Answer

Reweighting training samples based on sensitive attributes. Reweighting training samples based on sensitive attributes is a common pre-processing bias mitigation technique. It assigns higher weights to underrepresented groups or lower weights to overrepresented groups to balance the dataset, thereby reducing the model's reliance on biased correlations. This method directly addresses data-level bias before model training begins.

Answer

Using adversarial training

Answer

Applying L1 regularization

Answer

Performing k-fold cross-validation

Question 8

An AI system is being designed to automatically detect fraudulent transactions in real-time. The system must have low latency and high precision to minimize false alarms. Which algorithm is most appropriate?

Accepted Answer

Random forest. Random forest is the most appropriate algorithm because it handles high-dimensional transaction data, provides feature importance for interpretability, and achieves high precision with low latency through ensemble decision trees. Its parallelizable structure allows real-time scoring, and it naturally balances precision and recall without the computational overhead of deep learning.

Answer

Logistic regression

Answer

Convolutional neural network

Answer

Deep reinforcement learning

Question 9

Which THREE of the following are types of machine learning paradigms? (Choose three.)

Accepted Answer

Reinforcement learning. Reinforcement learning is a correct machine learning paradigm where an agent learns to make decisions by interacting with an environment, receiving rewards or penalties based on its actions. This trial-and-error approach is distinct from supervised and unsupervised learning, as it focuses on maximizing cumulative reward through exploration and exploitation.

Answer

Gradient boosting

Answer

Quantum computing

Question 10

A data scientist is training a neural network to classify images of handwritten digits. The model achieves 99% accuracy on training data but only 85% on validation data. Which technique should the scientist apply first to address this issue?

Accepted Answer

Apply L2 regularization to the network weights. The model shows high training accuracy (99%) but lower validation accuracy (85%), which is a classic sign of overfitting. L2 regularization (option C) adds a penalty term to the loss function proportional to the squared magnitude of the weights, discouraging the network from learning overly complex patterns that do not generalize. This directly addresses overfitting without reducing the model's capacity too aggressively.

Answer

Remove one or more hidden layers from the network

Answer

Increase the number of training epochs

Answer

Add more features to the input data

Question 11

A startup is building a chatbot to handle customer inquiries. They want the chatbot to understand context and provide accurate responses without requiring extensive labeled data. Which AI approach is most suitable?

Accepted Answer

Transfer learning with a pre-trained transformer model. Transfer learning with a pre-trained transformer model (e.g., BERT, GPT) is the most suitable approach because it allows the chatbot to understand context and generate accurate responses using knowledge learned from vast general-domain text, requiring only minimal fine-tuning on the startup's specific customer inquiry data. This eliminates the need for extensive labeled datasets, as the model already captures nuanced language patterns and contextual relationships through its self-attention mechanism.

Answer

Reinforcement learning from human feedback

Answer

Rule-based natural language processing

Answer

Convolutional neural networks (CNNs)

Question 12

Which TWO of the following are best practices for securing an AI model against adversarial attacks?

Accepted Answer

Adversarial training with perturbed examples.. Option B is correct because adversarial training explicitly augments the training dataset with perturbed examples (e.g., using FGSM or PGD attacks) to teach the model to recognize and resist malicious inputs. This method directly hardens the model against evasion attacks by improving its decision boundary robustness.

Answer

Model pruning to reduce the number of parameters.

Answer

Increasing model complexity to capture more patterns.

Answer

Hyperparameter optimization using grid search.

Question 13

A company deploys an AI model to predict equipment failure. The model performs well on historical data but fails to generalize to new data from a different factory. Which concept best describes this issue?

Accepted Answer

Overfitting. Option C (Overfitting) is correct because the model learned patterns specific to the historical data from the original factory, including noise and factory-specific nuances, rather than generalizable features. When applied to new data from a different factory, those learned patterns do not hold, causing poor performance. This is the classic symptom of overfitting: high accuracy on training data but low accuracy on unseen data.

Answer

Transfer learning

Answer

Underfitting

Answer

Bias-variance tradeoff

Question 14

Refer to the exhibit. A deep learning model is being trained. Based on the training log, which problem is most evident?

Accepted Answer

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

Answer

Vanishing gradients

Answer

Underfitting

Answer

Data leakage

Question 15

A large e-commerce company uses a recommendation system based on collaborative filtering. The system uses a matrix factorization model that is trained nightly on the entire user-item interaction history. Recently, the company launched a flash sale with thousands of new products. Users are reporting that the recommendations are not showing the new products, even for users who have purchased them during the sale. The data engineering team notices that the new products have very few interactions in the training data. The model's loss on the validation set has increased, and the recall@10 metric has dropped from 0.45 to 0.32. The team needs to improve the recommendation of new items without retraining the entire model from scratch every hour. Which approach should the team take?

Accepted Answer

Use a hybrid model that combines collaborative filtering with content-based features from product metadata. Option A is correct because a hybrid model that combines collaborative filtering with content-based features (e.g., product metadata like category, price, or description) can recommend new products even with zero or very few user interactions. The content-based component leverages item attributes to compute similarity between new and existing items, enabling the system to surface new products without requiring extensive interaction history. This approach addresses the cold-start problem for new items while preserving the collaborative filtering signal for established items, and it does not require retraining the entire model from scratch every hour.

Answer

Retrain the model every hour to incorporate new interactions quickly

Answer

Remove the new products from the recommendation pool until they accumulate enough interactions

Answer

Increase the number of latent factors in the matrix factorization model

Question 16

A machine learning team is deploying a model that predicts customer churn. They notice that the model's predictions are highly sensitive to small changes in input features, leading to inconsistent outputs. Which technique should the team apply to improve model stability?

Accepted Answer

Regularization. Regularization (Option C) is the correct technique because it adds a penalty term to the loss function (e.g., L1 or L2 regularization), which constrains the model's weights. This reduces variance and prevents overfitting to noise in the training data, directly addressing the high sensitivity to small input changes (brittleness). By shrinking coefficients, regularization forces the model to learn more general patterns, improving stability and consistency in predictions.

Answer

Increase learning rate

Answer

Feature scaling

Answer

Cross-validation

Question 17

A data scientist is training a convolutional neural network (CNN) for object detection. The training loss decreases rapidly but then plateaus at a high value, and the validation loss starts increasing. Which action should the scientist take to improve the model?

Accepted Answer

Reduce the model complexity. The training loss decreasing rapidly then plateauing at a high value while validation loss increases is classic overfitting. Reducing model complexity (Option C) directly addresses overfitting by decreasing the number of parameters or applying regularization (e.g., dropout, L2), which forces the network to learn more generalizable features rather than memorizing noise in the training data.

Answer

Increase the learning rate

Answer

Increase the number of epochs

Answer

Add more convolutional layers

Question 18

A data scientist is training a multi-class classifier with 10 classes. The training log shows the above output for the first two epochs. What is the most likely cause?

Accepted Answer

The learning rate is set to zero. When the learning rate is set to zero, the optimizer makes no updates to the model weights regardless of the computed gradients. The training loss remains constant across epochs because the parameters never change, which matches the log showing identical loss values for both epochs. This is a common debugging scenario where a misconfigured learning rate prevents any learning from occurring.

Answer

Batch normalization is disabled

Answer

The dataset is imbalanced

Answer

The model is overfitting

Question 19

A data scientist is preparing a dataset for training a classification model. The dataset contains 10,000 records with a binary target variable where 9,500 belong to class A and 500 belong to class B. Which technique should the scientist use to address the class imbalance?

Accepted Answer

SMOTE (Synthetic Minority Oversampling Technique). SMOTE is the correct technique because it generates synthetic samples for the minority class (class B) by interpolating between existing minority instances, effectively balancing the dataset without losing information. This approach avoids the overfitting risk of simple oversampling and the information loss of undersampling, making it ideal for a 19:1 imbalance ratio.

Answer

Random undersampling of class A

Answer

Adding Gaussian noise to class B

Answer

Principal Component Analysis (PCA)

Question 20

An engineer is building a regression model to predict housing prices. The dataset includes features such as square footage, number of bedrooms, and year built. The engineer notices that the square footage values range from 500 to 10,000, while the number of bedrooms ranges from 1 to 5. Which preprocessing step is most critical before training a gradient descent-based model?

Accepted Answer

Normalize or standardize the features. Gradient descent-based models are sensitive to the scale of input features because they update weights proportionally to the gradient, which is influenced by feature magnitudes. With square footage ranging 500–10,000 and bedrooms 1–5, the larger feature will dominate the gradient, causing slow or unstable convergence. Normalizing or standardizing (e.g., Z-score or min-max scaling) ensures all features contribute equally, leading to faster and more reliable training.

Answer

Use k-fold cross-validation

Answer

Apply log transformation to all features

Answer

One-hot encode the features

AI0-001 Practice Test 2 — 25 Questions

Exhibit

AI0-001 Practice Test 2 — 25 Questions

Exhibit