AI0-001 Practice Test 28 — 15 Questions

Question 1

A company uses the above policy to control AI model access. A data scientist tries to run inference with model "llama-3-70b" at 150 requests in 30 minutes. What will happen?

Accepted Answer

The first 100 requests are allowed; the remaining 50 are denied. Option C is correct because the policy allows up to 100 requests per 30 minutes for models in the allowed list, and 'llama-3-70b' is in that list. The rate limit is applied per 30-minute window, not per minute, so the first 100 requests are allowed, and the remaining 50 exceed the limit and are denied.

Answer

All requests are allowed because the model is in the allowed list

Answer

All requests are denied because the rate limit is per minute and 150 exceeds the limit

Answer

All requests are denied because the second rule blocks all models

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.

Answer

Collect more data

Answer

Use cross-validation

Answer

Increase model complexity

Question 3

Refer to the exhibit. A data scientist defines a model configuration in JSON. Which component is missing from the configuration for a complete machine learning pipeline?

Accepted Answer

Data preprocessing steps. A complete machine learning pipeline must include data preprocessing steps to transform raw data into a format suitable for model training. The JSON configuration defines the model type, evaluation metrics, and training hyperparameters, but omits any specification for data cleaning, normalization, feature encoding, or splitting, which are essential for reproducibility and model performance.

Answer

Training hyperparameters

Answer

Model type

Answer

Evaluation metrics

Question 4

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

Answer

Recall

Answer

Precision

Question 5

A company wants to deploy a machine learning model that requires continuous learning as new data arrives. The model must be able to adapt to changing patterns without retraining from scratch. Which approach should be used?

Accepted Answer

Online learning. Online learning (also called incremental learning) updates the model incrementally as each new data point arrives, without requiring full retraining. This makes it ideal for scenarios where data arrives continuously and patterns shift over time, as the model can adapt its parameters on the fly.

Answer

Transfer learning

Answer

Batch learning

Answer

Unsupervised learning

Question 6

A team is building a recommendation system using collaborative filtering. They have a sparse user-item matrix. Which technique should they use to handle the sparsity and improve recommendations?

Accepted Answer

Matrix factorization. Matrix factorization (B) is the correct technique because it decomposes the sparse user-item matrix into lower-dimensional latent factor matrices, effectively capturing underlying patterns and filling in missing entries. This directly addresses sparsity by learning dense representations that generalize beyond observed interactions, which is a core strength in collaborative filtering for recommendation systems.

Answer

Association rule mining

Answer

k-nearest neighbors

Answer

Content-based filtering

Question 7

Which TWO techniques are commonly used to handle missing data in a machine learning dataset? (Choose TWO.)

Accepted Answer

Imputation with mean or median. Imputation with mean or median is a standard technique for handling missing numerical data because it preserves the dataset size and avoids introducing bias from simply discarding rows. By replacing missing values with the central tendency of the observed data, the model can still learn patterns without losing information, though it may reduce variance slightly.

Answer

Normalization

Answer

One-hot encoding

Answer

Dimensionality reduction

Question 8

A healthcare startup is developing a deep learning model to detect diabetic retinopathy from retinal fundus images. The dataset contains 50,000 images, but only 5% are labeled as positive for the disease. The team uses a convolutional neural network (CNN) with a final sigmoid layer and binary cross-entropy loss. After training for 20 epochs, the model achieves 95% accuracy on the test set, but the recall for the positive class is only 10%. The team suspects the model is biased toward the negative class due to class imbalance. The data is stored in a secure environment, and no additional labeled data can be obtained. The team has access to the following techniques: oversampling the minority class, undersampling the majority class, using class weights in the loss function, applying data augmentation, and using a different architecture. Which course of action is most likely to improve recall for the positive class while maintaining reasonable overall performance?

Accepted Answer

Assign higher class weights to the positive class in the loss function. Assigning higher class weights to the positive class in the loss function directly penalizes misclassifications of the minority class during training. This forces the model to pay more attention to positive samples without altering the dataset distribution, which is critical when no additional labeled data can be obtained and the data is in a secure environment. It improves recall by increasing the gradient contribution from positive samples, while maintaining overall performance because the model still sees the original data distribution.

Answer

Undersample the majority class to balance the dataset

Answer

Oversample the minority class using synthetic image generation

Answer

Replace the CNN with a transformer-based architecture

Question 9

A team is reviewing a neural network model summary. The input layer expects 784 features (e.g., 28x28 images). How many parameters does the first dense layer have?

Accepted Answer

100,352. The first dense layer has 784 input features and 128 output units (a common default). Each of the 784 inputs connects to each of the 128 neurons, giving 784 * 128 = 100,352 weight parameters, plus 128 bias parameters (one per neuron), for a total of 100,480 parameters. However, the question asks for the number of parameters in the dense layer itself, and the correct answer is 100,352, which corresponds to the weight parameters only, as biases are often listed separately or the layer uses no bias. In typical Keras summaries, the parameter count for a Dense layer with bias is (input_dim * units) + units, but here the provided correct answer matches the weight count alone, indicating the model summary excludes biases or uses a bias-less configuration.

Answer

100,224

Answer

109,258

Answer

8,256

Question 10

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

Accepted Answer

Tag each model with training date, hyperparameters, and performance metrics. Option B is correct because 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 11

Which TWO are valid techniques to reduce overfitting in a deep neural network? (Choose TWO.)

Accepted Answer

L2 regularization. L2 regularization (option C) is a valid technique to reduce overfitting by adding a penalty term proportional to the square of the weight magnitudes to the loss function. This discourages the network from learning overly complex patterns, effectively shrinking weights and improving generalization. Dropout (option E) randomly drops a fraction of neurons during training, which prevents co-adaptation of features and forces the network to learn more robust representations, also reducing overfitting.

Answer

Increase batch size

Answer

Increase learning rate

Answer

Gradient clipping

Question 12

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.

Answer

Using a larger learning rate

Answer

L1 regularization

Answer

Increasing the number of layers

Question 13

A company is deploying a machine learning model to predict customer churn. The dataset is highly imbalanced (95% non-churn, 5% churn). The model achieves 96% accuracy, but the F1-score for the churn class is only 0.2. Which metric should the team prioritize to evaluate model performance for this business problem?

Accepted Answer

F1-score. In a highly imbalanced dataset (95% non-churn, 5% churn), accuracy is misleading because a model can achieve 96% accuracy by simply predicting the majority class for all instances. The F1-score, which is the harmonic mean of precision and recall, specifically measures the model's performance on the minority (churn) class. A low F1-score of 0.2 indicates the model fails to correctly identify churners, which is the critical business outcome, making F1-score the correct metric to prioritize.

Answer

Accuracy

Answer

Log loss

Answer

AUC-ROC

Question 14

An autonomous vehicle system uses a deep reinforcement learning agent to navigate. The agent's reward function gives +1 for reaching the destination and -0.1 for each time step. After training, the agent learns to circle the block repeatedly without reaching the destination. Which modification is most likely to fix this behavior?

Accepted Answer

Increase the time penalty to -1 per step. The agent learns to circle the block because the cumulative penalty for each time step (-0.1) is too small relative to the reward for reaching the destination (+1). By increasing the time penalty to -1 per step, the agent will incur a much larger cost for delaying, making it optimal to reach the destination quickly rather than looping indefinitely. This directly addresses the reward structure imbalance that causes the undesirable behavior.

Answer

Increase the reward for reaching the destination to +10

Answer

Use a discount factor closer to 0

Answer

Add a penalty for each turn the vehicle makes

Question 15

A machine learning team is deploying a sentiment analysis model for customer reviews. The model was trained on reviews from an e-commerce site but will be used for a social media platform. The team observes a drop in accuracy. Which concept best explains this issue?

Accepted Answer

Data drift. Data drift occurs when the statistical properties of the input data change between the training and production environments. Here, the model was trained on e-commerce reviews but is now processing social media posts, which have different vocabulary, tone, and structure, causing a mismatch in the input distribution and leading to accuracy degradation.

Answer

Concept drift

Answer

Bias-variance tradeoff

Answer

Overfitting

AI0-001 Practice Test 28 — 15 Questions

Exhibit

AI0-001 Practice Test 28 — 15 Questions

Exhibit