AI0-001 Practice Test 8 — 10 Questions

Question 1

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?

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

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.

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Increase the reward for reaching the destination to +10

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Use a discount factor closer to 0

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Add a penalty for each turn the vehicle makes

Question 2

A data scientist trains a deep neural network for image classification. The training loss decreases but validation loss starts increasing after 50 epochs. What should the data scientist do to improve generalization?

Accepted Answer

Apply dropout and early stopping. The increasing validation loss while training loss decreases is a classic sign of overfitting. Dropout randomly deactivates neurons during training, which prevents co-adaptation and forces the network to learn more robust features. Early stopping halts training when validation performance stops improving, directly addressing the overfitting by selecting the model with the best generalization before it degrades.

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Decrease batch size

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Add more hidden layers

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Increase learning rate

Question 3

A company wants to deploy a chatbot that uses natural language understanding (NLU) to answer customer queries. Which AI technique is most suitable for understanding the intent of user input?

Accepted Answer

Sequence-to-sequence model with attention. Sequence-to-sequence models with attention are specifically designed to handle variable-length input sequences (like user queries) and map them to output sequences (like intent labels or responses). The attention mechanism allows the model to focus on the most relevant parts of the input when determining intent, which is critical for understanding nuanced or long user queries in NLU tasks.

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K-means clustering

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Linear regression

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Decision tree

Question 4

A data scientist is preparing a dataset for a classification task. The dataset contains 10,000 rows and 50 features, but many features have missing values. Which approach should the scientist take first to address the missing data?

Accepted Answer

Analyze the pattern and proportion of missing values to choose an appropriate imputation strategy.. The first step in handling missing data is to understand the pattern and proportion of missingness (e.g., MCAR, MAR, MNAR) to select an appropriate imputation method. Blindly applying imputation or deletion without analysis can introduce bias or reduce model performance. This diagnostic step ensures the chosen strategy aligns with the data's underlying structure and the classification task's requirements.

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Use a deep learning model to predict missing values without preprocessing.

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Remove all rows with any missing values to ensure a clean dataset.

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Replace missing values with the mean of each feature immediately.

Question 5

Refer to the exhibit. The data scientist notices that the model achieves 98% accuracy on the training set but only 72% on the test set. Which change to the model parameters is most likely to reduce this gap?

Accepted Answer

Reduce max_depth to 3.. The model is overfitting: 98% training accuracy vs. 72% test accuracy. Reducing max_depth to 3 limits the depth of each decision tree, preventing them from memorizing noise and forcing them to learn more generalizable patterns. This is a standard regularization technique for tree-based ensembles.

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Increase n_estimators to 500.

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Set max_depth to None to allow trees to grow fully.

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Switch from RandomForest to a linear model like LogisticRegression.

Question 6

A data scientist wants to group customers into segments based on purchasing behavior without predefined labels. Which type of machine learning is most appropriate?

Accepted Answer

Unsupervised learning. Unsupervised learning is the correct choice because the data scientist has no predefined labels and wants to discover natural groupings in customer purchasing behavior. Clustering algorithms, such as K-means or DBSCAN, are used in unsupervised learning to segment data based on inherent patterns without any target variable.

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Reinforcement learning

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Supervised learning

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Semi-supervised learning

Question 7

A team is training a deep learning model for natural language processing using a large corpus. They notice the model has a very high number of parameters and training is slow. Which technique can reduce the number of parameters without significant performance loss?

Accepted Answer

Apply embedding compression. Embedding compression reduces the dimensionality of the embedding layer, which often contains the majority of the model's parameters in NLP tasks. By using techniques like low-rank factorization or pruning, the model retains most of its representational power while significantly decreasing the parameter count and training time.

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Add more dropout layers

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Use a larger batch size

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Increase learning rate

Question 8

A company wants to use AI to analyze customer reviews and determine sentiment (positive, negative, neutral). Which AI subfield is most directly applicable?

Accepted Answer

Natural language processing. Natural language processing (NLP) is the AI subfield that enables machines to understand, interpret, and generate human language. Analyzing customer reviews for sentiment requires processing text, extracting meaning, and classifying it as positive, negative, or neutral, which is a core NLP task called sentiment analysis.

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Reinforcement learning

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Computer vision

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Robotics

Question 9

A research team is training a deep neural network for image classification. The training loss decreases rapidly for the first few epochs but then plateaus, while validation loss starts to increase after epoch 10. Which action would best address this issue?

Accepted Answer

Implement early stopping based on validation loss to prevent further overfitting.. The training loss decreasing rapidly then plateauing while validation loss increases after epoch 10 is a classic sign of overfitting. Early stopping monitors validation loss and halts training when it begins to rise, preventing the model from memorizing noise in the training data. This directly addresses the overfitting issue without requiring architectural or hyperparameter changes that could destabilize training.

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Reduce the batch size to introduce more noise during training.

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Increase the learning rate to help the model escape the plateau.

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Add more convolutional layers to increase model capacity.

Question 10

A data scientist is deploying a machine learning model to production. The model was trained on an imbalanced dataset. Which technique should be used during deployment to mitigate bias without retraining the model?

Accepted Answer

Apply post-processing calibration to adjust decision thresholds. Post-processing calibration adjusts the decision threshold of the model to account for the class imbalance present in the training data. This technique modifies the output probabilities or classification boundary without requiring access to the original training data or retraining the model, making it suitable for deployment scenarios where the model is already fixed.

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Use an ensemble of models trained on balanced subsets

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Rebalance the dataset using SMOTE before inference

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Remove sensitive features from the input data