AI0-001 AI Models and Data Engineering — Timed 30 Questions

Question 1

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.

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Random undersampling of class A

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Adding Gaussian noise to class B

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Principal Component Analysis (PCA)

Question 2

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.

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Use k-fold cross-validation

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Apply log transformation to all features

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One-hot encode the features

Question 3

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.

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Concept drift

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Bias-variance tradeoff

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Overfitting

Question 4

A data engineer needs to design a data pipeline for a real-time fraud detection system. The system requires low-latency processing of streaming transactions. Which architecture is most appropriate?

Accepted Answer

Stream processing with Apache Kafka and Flink. Apache Kafka provides a distributed, fault-tolerant event streaming platform that ingests high-throughput transaction data with low latency, while Apache Flink offers true stream processing with exactly-once semantics and sub-second event-time processing. Together, they enable real-time fraud detection by analyzing transactions as they arrive, without the delays inherent in batch or micro-batch approaches.

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Data lake with Apache Spark

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Batch processing with Apache Hadoop

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Microservices architecture with REST APIs

Question 5

A team is training a deep learning model for image classification. The training loss decreases rapidly but validation loss starts increasing after a few epochs. Which regularization technique should be applied to mitigate this issue?

Accepted Answer

Early stopping. Option C is correct because early stopping halts training when validation loss increases, preventing overfitting. Option A is wrong because L2 regularization penalizes large weights but doesn't stop training. Option B is wrong because dropout randomly drops neurons during training, but early stopping directly addresses the symptom. Option D is wrong because data augmentation increases data diversity, but the issue is overfitting due to training too long.

Answer

Data augmentation

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L2 regularization

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Dropout

Question 6

An organization needs to store sensitive customer data for training a machine learning model. The data must be encrypted at rest and in transit, and access must be audited. Which combination of practices should be implemented?

Accepted Answer

Use TLS for transfer, AES-256 for storage, and AWS CloudTrail for auditing. Option A is correct because it combines TLS (Transport Layer Security) for encrypting data in transit, AES-256 for strong encryption at rest, and AWS CloudTrail for auditing API-level access. TLS ensures confidentiality and integrity during transmission, AES-256 provides robust symmetric encryption for stored data, and CloudTrail logs all AWS API calls for compliance and audit trails. This triad satisfies the requirements of encryption in transit, at rest, and audited access.

Answer

Use FTP for transfer, AES-128 for storage, and manual log review

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Use SSH for transfer, store data in a database, and enable access logs

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Use MD5 for hashing, store data in plaintext, and enable server logs

Question 7

A data analyst is cleaning a dataset and finds that 20% of the values for the 'age' column are missing. Which imputation method is most robust if the data is not normally distributed?

Accepted Answer

Median imputation. Median imputation is the most robust method for handling missing values in the 'age' column when the data is not normally distributed because the median is unaffected by outliers or skewness. Unlike the mean, which is sensitive to extreme values, the median provides a central tendency measure that better represents the typical value in non-normal distributions, preserving the dataset's integrity for downstream modeling.

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Mean imputation

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Mode imputation

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Remove rows with missing values

Question 8

Which TWO techniques are commonly used for feature selection in machine learning? (Choose 2)

Accepted Answer

L1 regularization (Lasso). L1 regularization (Lasso) is correct because it adds a penalty equal to the absolute value of the magnitude of coefficients, which can shrink some coefficients exactly to zero, effectively performing feature selection by removing irrelevant features from the model. This makes it a built-in feature selection technique within the training process.

Answer

Principal Component Analysis (PCA)

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SMOTE

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Dropout

Question 9

Which THREE are common data preprocessing steps in a machine learning pipeline? (Choose 3)

Accepted Answer

Encoding categorical variables. Encoding categorical variables is a common data preprocessing step because machine learning algorithms require numerical input. Techniques like one-hot encoding or label encoding convert categorical data (e.g., colors, countries) into numeric format, enabling the model to process them correctly. Without this step, the model would misinterpret categorical labels as ordinal or meaningless numeric values.

Answer

Hyperparameter tuning

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Model evaluation

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.

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Use the same model version for all deployments

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Store only the final model binary without metadata

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Manually rename model files with version numbers

Question 11

An engineer is training a neural network and observes the output shown. Which conclusion is most likely correct?

Accepted Answer

The model is overfitting after epoch 2.. The output shows training loss decreasing while validation loss increases after epoch 2, which is a classic sign of overfitting. The model begins to memorize the training data rather than generalize, leading to poor performance on unseen data. This pattern confirms that overfitting starts after epoch 2, making option B correct.

Answer

The gradients are vanishing.

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The model is underfitting.

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The learning rate is too high.

Question 12

A data engineer is reviewing an S3 bucket policy for a machine learning project. The policy is intended to allow access to training data only from the corporate network (10.0.0.0/16). However, users in the corporate network report access denied. Which issue is most likely causing the problem?

Accepted Answer

The policy does not include s3:ListBucket action, which is needed to list objects.. Option C is correct. The condition key uses "aws:SourceIp" but the correct key for IP address condition is "aws:SourceIp". However, the exhibit shows "aws:SourceIp" which is correct? Wait, the exhibit shows "aws:SourceIp" — that is correct. But the problem might be that the policy uses "GetObject" but the users might be trying to list objects (s3:ListBucket). Option C says the action does not include s3:ListBucket, which is often needed first. Option A is wrong because the resource is correct. Option B is wrong because the policy allows access from 10.0.0.0/16. Option D is wrong because the effect is Allow.

Answer

The policy is missing a Deny statement.

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The resource ARN is incorrect.

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The IP address condition is not matching because the corporate network uses a different CIDR.

Question 13

A data scientist is training a deep learning model for image classification. The training loss decreases steadily but the validation loss starts increasing after 10 epochs. Which technique should the scientist apply to address this issue?

Accepted Answer

Implement early stopping. The scenario describes overfitting: the model memorizes training data (loss decreases) but fails to generalize to unseen validation data (validation loss increases). Early stopping (Option C) halts training when validation performance degrades, preventing overfitting while preserving the best model weights. This is a standard regularization technique in deep learning frameworks like TensorFlow and PyTorch.

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Add more dropout layers

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Reduce the learning rate

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Increase the number of training epochs

Question 14

A financial institution is building a fraud detection system using a supervised learning model. The dataset is highly imbalanced with 99.9% legitimate transactions and 0.1% fraudulent ones. Which approach would be MOST effective to train the model to detect fraud?

Accepted Answer

Use SMOTE to generate synthetic fraudulent transactions. SMOTE (Synthetic Minority Oversampling Technique) is the most effective approach because it generates synthetic fraudulent transactions by interpolating between existing minority class samples, thereby balancing the dataset without losing information. This allows the model to learn decision boundaries for fraud detection more effectively than simple undersampling or metric adjustments, especially given the extreme 99.9% vs 0.1% imbalance.

Answer

Train the model using accuracy as the performance metric

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Undersample the legitimate transactions to match the number of fraudulent ones

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Increase the regularization strength in the model

Question 15

A company wants to deploy an AI model for real-time inference on edge devices with limited computational resources. Which model architecture would be MOST suitable?

Accepted Answer

MobileNet. MobileNet is specifically designed for mobile and edge devices using depthwise separable convolutions, which drastically reduce the number of parameters and computational cost while maintaining acceptable accuracy. This makes it the most suitable choice for real-time inference on resource-constrained edge hardware.

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YOLOv4

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ResNet-152

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BERT

Question 16

A data engineer is designing a pipeline for a streaming data application that uses a machine learning model to detect anomalies in real time. Which TWO practices should the engineer implement to ensure data quality and model reliability?

Accepted Answer

Use a sliding window for feature computation. Option C is correct because streaming anomaly detection requires real-time feature computation over recent data, and a sliding window ensures that only the most relevant data points are used for model inference, maintaining low latency and adapting to concept drift. This approach avoids the staleness of batch processing and aligns with the continuous nature of streaming pipelines.

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Use batch processing to transform data in fixed intervals

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Store all raw data indefinitely for future analysis

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Retrain the model on a fixed schedule every 24 hours

Question 17

A team is developing a natural language processing model to classify customer feedback. The dataset contains text in multiple languages. Which THREE preprocessing steps are essential to ensure the model performs well across all languages?

Accepted Answer

Lowercasing. Lowercasing is essential because it normalizes text across languages by converting all characters to the same case, reducing vocabulary size and ensuring that words like 'Good' and 'good' are treated identically. This prevents the model from learning separate representations for case variations, which is critical for multilingual datasets where case usage may differ (e.g., German capitalizes nouns). Without lowercasing, the model's performance degrades due to sparsity and increased feature space.

Answer

One-hot encoding

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Stemming

Question 18

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

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

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Increase the number of latent factors in the matrix factorization model

Question 19

A healthcare startup is developing a deep learning model to detect diabetic retinopathy from retinal images. The model is trained on a dataset of 10,000 labeled images. During initial testing, the model achieves 99% accuracy on the training set but only 85% on the test set. The startup wants to deploy the model in a clinical setting where false negatives (missing a disease) are critical. The team has access to additional unlabeled retinal images from multiple sources. Which strategy should the team use to improve the model's generalization and reduce false negatives?

Accepted Answer

Use semi-supervised learning with the unlabeled images to improve feature representations. Semi-supervised learning leverages the large pool of unlabeled retinal images to learn robust feature representations, which helps the model generalize better to unseen data. By reducing overfitting (the gap between 99% training and 85% test accuracy), this approach directly improves test-set performance. Additionally, semi-supervised methods can be tuned to emphasize recall, thereby reducing false negatives critical in clinical diabetic retinopathy screening.

Answer

Apply aggressive data augmentation to the training set

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Increase the learning rate during training

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Add more convolutional layers to the model

Question 20

A data scientist is preparing a dataset for a classification model. The dataset contains a column "Age" with 10% missing values and a column "Income" with 30% missing values. Which imputation strategy is MOST appropriate to minimize bias?

Accepted Answer

Replace missing Age with the mean and missing Income with the median.. Option A is correct because using mean imputation for Age (10% missing) and median imputation for Income (30% missing) minimizes bias. Mean is suitable for roughly symmetric distributions with low missingness, while median is robust to outliers and skewness, which is common in income data. This combination reduces distortion of central tendency and preserves data integrity better than uniform methods.

Answer

Delete all rows with missing values.

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Replace missing Age with the mode and missing Income with a constant value.

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Replace missing values with zeros.