AI0-001 AI Concepts and Foundations — Timed 15 Questions

Question 1

An AI engineer trains a deep learning model for image classification. After training, the training accuracy is 99% but validation accuracy is 85%. Which technique would best address this discrepancy?

Accepted Answer

Add dropout layers. The high training accuracy (99%) and lower validation accuracy (85%) indicate overfitting, where the model memorizes training data but fails to generalize. Dropout layers randomly deactivate neurons during training, forcing the network to learn more robust features and reducing overfitting. This technique directly addresses the discrepancy by improving validation performance without sacrificing training capacity.

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Increase data augmentation

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Decrease the learning rate

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Increase the number of layers

Question 2

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.

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Vanishing gradients

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Underfitting

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Data leakage

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.

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Training hyperparameters

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Model type

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Evaluation metrics

Question 4

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. 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.

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

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

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All requests are denied because the second rule blocks all models

Question 5

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.

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Logistic regression

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Convolutional neural network

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Deep reinforcement learning

Question 6

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 7

Which three techniques are commonly used to mitigate overfitting in neural networks? (Choose three.)

Accepted Answer

Adding L2 regularization. Option A (Adding L2 regularization) is correct because penalizing large weights via an L2 term in the loss function constrains model complexity and reduces overfitting. Option C (Dropout) is correct because randomly deactivating neurons during training prevents co-adaptation and acts as an ensemble-like regularizer. Option E (Early stopping) is correct because halting training when validation loss stops improving prevents the model from memorizing training noise. Option B (Increasing training data) is not among the marked correct answers here, and Option D (Reducing number of layers) is not marked correct either, as it is a capacity-reduction approach rather than one of the three techniques specified in this question's answer key.

Answer

Increasing training data

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Reducing number of layers

Question 8

Based on the exhibit, what issue should the team address?

Accepted Answer

Potential fairness bias across groups. The exhibit likely shows a confusion matrix or performance metrics broken down by demographic groups (e.g., race, gender), revealing that the model's false positive or false negative rates differ significantly across groups. This disparity indicates a potential fairness bias, which must be addressed to ensure equitable outcomes, especially in high-stakes AI applications like hiring or lending.

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Model accuracy below threshold

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High latency

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Low throughput

Question 9

A data scientist is building a natural language processing model to classify customer reviews as positive or negative. Which TWO preprocessing steps are most essential before tokenization? (Select two.)

Accepted Answer

Remove punctuation and special characters.. Removing punctuation and special characters (Option B) is essential because tokenizers typically split on whitespace, so punctuation attached to words (e.g., 'great!', 'bad.') would create noisy tokens like 'great!' and 'bad.' instead of clean tokens 'great' and 'bad'. Converting all text to lowercase (Option C) ensures that words like 'Great', 'great', and 'GREAT' are all mapped to the same token, preventing the model from treating them as distinct features and reducing vocabulary size.

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Perform stemming or lemmatization.

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Remove stop words from the text.

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Replace missing values with a placeholder.

Question 10

Refer to the exhibit. The model is a neural network for 10-class classification. The training log shows no improvement over 5 epochs. Which of the following is the most likely root cause?

Accepted Answer

The model is suffering from vanishing gradients, preventing weight updates.. The training log shows no improvement over 5 epochs, which is a classic symptom of vanishing gradients in deep neural networks. When gradients become extremely small during backpropagation, weight updates are negligible, causing the loss to stagnate. This is especially common in deep networks with sigmoid or tanh activations, where gradients saturate in the tails of the activation function.

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The batch size is too large, making gradient updates insignificant.

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The output layer uses sigmoid activation instead of softmax.

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The learning rate is too high, causing the loss to oscillate.

Question 11

A data scientist is training a model to classify customer support tickets into categories. The dataset has 10,000 labeled examples, but the 'billing' category contains 8,000 examples while the 'technical' category contains 2,000. Which technique is most appropriate to address this imbalance before training?

Accepted Answer

Apply random oversampling on the 'technical' category.. Random oversampling duplicates examples from the minority class ('technical') to balance the class distribution, preventing the model from becoming biased toward the majority class ('billing'). This technique directly addresses the class imbalance before training, which is critical for classification tasks where the minority class is underrepresented.

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Remove all examples except 'billing' and use a one-class classifier.

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Use accuracy as the only evaluation metric.

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Train the model as is, then adjust thresholds post-training.

Question 12

Which metric is most appropriate for evaluating a binary classification model where the positive class is rare and false positives are costly?

Accepted Answer

Precision. Precision is the most appropriate metric when the positive class is rare and false positives are costly because it measures the proportion of true positive predictions among all positive predictions. In this scenario, minimizing false positives is critical, and precision directly penalizes them by requiring high confidence before labeling an instance as positive. This aligns with the business need to avoid costly false alarms, such as in fraud detection or medical diagnosis for rare diseases.

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Accuracy

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F1-score

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Recall

Question 13

Refer to the exhibit. A system administrator reviews the deployment. Which action should be taken to meet the SLA?

Accepted Answer

Scale up the compute resources. The exhibit shows a deployment where inference latency exceeds the SLA requirement. Scaling up compute resources (e.g., adding more CPU cores, GPU memory, or increasing instance size) directly reduces per-request processing time by providing more parallel processing capacity, which is the most straightforward way to meet latency SLAs when the model is already optimized.

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Retrain the model

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Implement caching

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Reduce model input size

Question 14

A company is building a recommendation system for an e-commerce platform. They want the system to learn from user purchase history and browsing behavior to suggest products. Which type of machine learning is most appropriate for this task?

Accepted Answer

Unsupervised learning. Unsupervised learning is the most appropriate because the system must discover hidden patterns and groupings in user purchase history and browsing behavior without labeled outcomes. Recommendation systems often use clustering or association rule mining (e.g., market basket analysis) to identify product affinities and user segments, which are core unsupervised techniques. This allows the system to suggest products based on learned co-occurrence patterns rather than predefined categories.

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Supervised learning

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Semi-supervised learning

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Transfer learning

Question 15

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.

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Recall

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Accuracy

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Precision