AIF-C01 AI and ML Fundamentals — 25 Questions

Question 1

A data scientist needs to predict house prices based on features like square footage, number of bedrooms, and location. Which type of machine learning is most appropriate for this task?

Accepted Answer

Regression. Regression is the correct choice because the task involves predicting a continuous numerical value (house price) based on input features. Unlike classification, which predicts discrete categories, regression models the relationship between independent variables and a continuous target, making it ideal for price prediction.

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Classification

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Reinforcement learning

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Clustering

Question 2

A machine learning model achieves 99% accuracy on the training set but only 65% on the test set. Which phenomenon is the model experiencing?

Accepted Answer

Overfitting. The model's high accuracy on the training set (99%) but significantly lower accuracy on the test set (65%) indicates that it has memorized the training data, including noise and outliers, rather than learning generalizable patterns. This is the classic symptom of overfitting, where the model performs well on seen data but fails to generalize to unseen data.

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Bias-variance tradeoff

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Data leakage

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Underfitting

Question 3

A financial services firm must extract text from scanned loan application forms to automate data entry. The forms are in various languages. Which combination of AWS AI services should be used?

Accepted Answer

Amazon Textract and Amazon Translate. Amazon Textract is designed to extract text and data from scanned documents, including forms, using OCR (Optical Character Recognition). Amazon Translate then converts the extracted text from various languages into a target language, enabling automated data entry across multilingual loan applications. Together, they directly address the requirement of extracting text from scanned forms in multiple languages.

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Amazon Transcribe and Amazon Polly

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Amazon Kendra and Amazon Lex

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Amazon Rekognition and Amazon Comprehend

Question 4

A company wants to build a recommendation system for its e-commerce website. Historical data includes user purchase history, product categories, and ratings. Which AWS service is most suitable for this task?

Accepted Answer

Amazon Personalize. Amazon Personalize is the correct choice because it is a fully managed AWS service specifically designed to build real-time recommendation systems using user-item interaction data, such as purchase history, product categories, and ratings. It uses machine learning to train and deploy personalized recommendation models without requiring you to manage infrastructure or write custom ML code, making it ideal for e-commerce recommendation scenarios.

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Amazon Forecast

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Amazon SageMaker built-in algorithms

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Amazon Comprehend

Question 5

A binary classification model outputs probabilities. The default threshold of 0.5 results in high precision but low recall. Which action would likely increase recall while maintaining acceptable precision?

Accepted Answer

Decrease the threshold to 0.3. Decreasing the threshold to 0.3 makes the model classify more instances as positive, which increases recall (more true positives captured) but may also increase false positives. The goal is to shift the precision-recall trade-off toward higher recall while keeping precision at an acceptable level, which is directly achieved by lowering the decision threshold.

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Use F1 score instead of accuracy

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Apply oversampling to the minority class

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Increase the threshold to 0.7

Question 6

A data scientist is training a neural network for image classification. The loss decreases rapidly for the first few epochs but then plateaus. Which technique is most likely to help the model continue improving?

Accepted Answer

Reduce the learning rate using a scheduler. When the loss plateaus after rapid initial decrease, the model is likely oscillating around a local minimum because the learning rate is too high for finer convergence. A learning rate scheduler (e.g., StepLR, ReduceLROnPlateau) reduces the step size over time, allowing the optimizer to take smaller, more precise steps and escape the plateau to continue improving.

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Increase the number of epochs

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Increase the batch size

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Add more dropout layers

Question 7

A company uses Amazon Rekognition to detect objects in images. They notice that the model frequently misidentifies a rare object as a common background item. What is the most likely cause?

Accepted Answer

Class imbalance in the training data. Amazon Rekognition is a pre-trained service, not a custom model you train yourself, so issues like insufficient epochs or overfitting do not apply. When a rare object is frequently misidentified as a common background item, the most likely cause is class imbalance in the training data used to build the underlying model, where the rare object appears far less often than common items, causing the model to bias predictions toward the majority class.

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Insufficient training epochs

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Overfitting on the rare object

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The confidence threshold is set too high

Question 8

A data scientist is preparing features for a linear regression model. One feature 'income' has values ranging from $15,000 to $350,000, while another 'age' ranges from 18 to 70. Which feature engineering step is most important?

Accepted Answer

Standardization (z-score scaling). Standardization (z-score scaling) is the most important step because linear regression models are sensitive to the scale of features. With 'income' ranging from $15,000 to $350,000 and 'age' from 18 to 70, the large magnitude difference can cause the model to assign disproportionate importance to 'income' during gradient descent optimization, leading to unstable or biased coefficient estimates. Z-score scaling transforms both features to have a mean of 0 and standard deviation of 1, ensuring equal contribution and improving convergence.

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Polynomial feature creation

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One-hot encoding

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Feature selection using mutual information

Question 9

Which AWS service can convert a text document from one language to another?

Accepted Answer

Amazon Translate. Amazon Translate is a neural machine translation service that delivers fast, high-quality, and customizable language translation. It uses deep learning models to translate text documents from a source language to a target language, making option D the correct choice for this task.

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Amazon Polly

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Amazon Textract

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Amazon Comprehend

Question 10

A team is building a classifier to detect fraudulent transactions. The dataset has 99.9% legitimate transactions and 0.1% fraudulent. Which evaluation metric is most appropriate?

Accepted Answer

AUC-ROC. AUC-ROC is the most appropriate metric for this highly imbalanced classification problem because it evaluates the model's ability to distinguish between fraudulent (positive) and legitimate (negative) transactions across all classification thresholds, without being biased by the overwhelming majority of legitimate transactions. Unlike accuracy, AUC-ROC focuses on the trade-off between true positive rate and false positive rate, making it robust to class imbalance where 99.9% of data is negative.

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Mean Absolute Error (MAE)

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Root Mean Squared Error (RMSE)

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Accuracy

Question 11

A data scientist is building a model to predict customer churn. They have 10,000 samples with 20 features. After training a decision tree, they observe high variance. Which ensemble method would best reduce variance without significantly increasing bias?

Accepted Answer

Random forest. A random forest reduces variance by training multiple decision trees on bootstrapped subsets of the data and averaging their predictions. This ensemble approach leverages the low bias of individual trees while smoothing out their high variance through aggregation, directly addressing the overfitting issue without substantially increasing bias.

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k-nearest neighbors

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Gradient boosting

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Logistic regression

Question 12

A company wants to analyze customer feedback to identify common themes and sentiment. The feedback is in multiple languages. Which AWS services should be used together?

Accepted Answer

Amazon Translate and Amazon Comprehend. Amazon Translate provides real-time and batch language translation, enabling the conversion of multilingual customer feedback into a single target language. Amazon Comprehend then performs natural language processing (NLP) on the translated text to identify key themes, entities, and sentiment. Together, they form a complete pipeline for analyzing feedback across multiple languages.

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Amazon Transcribe and Amazon Comprehend

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Amazon Polly and Amazon Translate

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Amazon Rekognition and Amazon Comprehend

Question 13

A data scientist is preparing data for a classification model. The dataset contains missing values in several features. Which TWO approaches are appropriate for handling missing data? (Select TWO.)

Accepted Answer

Remove rows with any missing values. Imputation fills missing values with estimated values (e.g., mean, median, mode), allowing the model to use all samples. Removing rows with missing values is a simple deletion approach.

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Set missing values to zero

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Ignore missing values during training

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Replace missing values with -1

Question 14

A company wants to build a system that automatically routes support tickets to the appropriate department based on the text description. They have labeled historical data. Which THREE AWS services could be used to implement this solution? (Select THREE.)

Accepted Answer

Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that can analyze text to extract insights such as sentiment, key phrases, and custom classification. By training a custom classifier on labeled historical support ticket data, Comprehend can automatically route tickets to the appropriate department based on the text description, making it a direct fit for this use case.

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Amazon Rekognition

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Amazon Polly

Question 15

A data scientist is evaluating a regression model and wants to understand its prediction errors. Which TWO metrics should they use? (Select TWO.)

Accepted Answer

Mean Absolute Error (MAE). Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are both standard metrics for evaluating regression model prediction errors. MAE measures the average absolute difference between predicted and actual values, giving equal weight to all errors, while RMSE squares the errors before averaging, penalizing larger errors more heavily. Together, they provide complementary insights into the magnitude and distribution of residuals.

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F1 score

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Accuracy

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Precision

Question 16

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. Retrieval-Augmented Generation (RAG) is the most appropriate approach because it allows the chatbot to answer questions based on the latest policy documents without retraining the underlying language model. By indexing the documents in a vector store and retrieving relevant chunks at query time, RAG provides up-to-date, grounded responses while keeping the base LLM static, which is both cost-effective and scalable for monthly document updates.

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Fine-tune a base LLM on the policy documents monthly

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Use a larger foundation model with a longer context window and paste all documents into each prompt

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Train a custom model from scratch on the policy documents each month

Question 17

Which AWS service is BEST suited for extracting text from scanned PDF documents, such as invoices and receipts?

Accepted Answer

Amazon Textract. Amazon Textract is specifically designed to extract text, handwriting, and data from scanned documents like invoices and receipts. It uses machine learning to recognize and extract printed text, forms, and tables from document images, going beyond simple OCR by understanding the structure of the document.

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Amazon Comprehend

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Amazon Rekognition

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Amazon Transcribe

Question 18

A data scientist trains a binary classification model and obtains the following results on the test set: accuracy 0.92, precision 0.90, recall 0.85, F1 0.87. The dataset has 5% positive class. The business requirement is to minimize false negatives. Which metric should the team prioritize?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positives correctly identified, which directly addresses the business requirement to minimize false negatives. With a 5% positive class, accuracy is misleadingly high because the model can simply predict the majority class (negative) and still achieve 95% accuracy, but this would result in zero recall. Prioritizing recall ensures the model captures as many true positives as possible, reducing false negatives at the cost of potentially more false positives.

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F1 score

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Accuracy

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Precision

Question 19

A team is using Amazon SageMaker to train a deep learning model. They notice that the training loss decreases steadily but the validation loss starts increasing after 10 epochs. Which technique should they apply to address this issue?

Accepted Answer

Apply early stopping. The scenario describes overfitting, where the model memorizes training data but fails to generalize to validation data. Early stopping halts training when validation loss stops improving, preventing overfitting while preserving the best model weights. This is a standard regularization technique in SageMaker training jobs, configurable via the `use_early_stopping` parameter in the `Estimator` or `HyperparameterTuner`.

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Reduce the batch size

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Increase the learning rate

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Add more layers to the network

Question 20

A company wants to forecast product demand across thousands of SKUs with different demand patterns. They have 3 years of historical sales data, plus external factors like holidays and promotions. Which combination of AWS services and approach would deliver the most accurate forecasts with minimal manual effort?

Accepted Answer

Use Amazon Forecast with the DeepAR+ algorithm and provide item metadata, holiday calendars, and promotion data. Amazon Forecast is purpose-built for time-series forecasting and automatically handles multiple SKUs, holidays, and promotions. SageMaker would require building custom models from scratch. Comprehend is NLP, and QuickSight is for visualization.

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Use Amazon Comprehend to analyze customer reviews and correlate with sales

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Upload data to Amazon QuickSight and use its built-in forecasting widget

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Train individual ARIMA models for each SKU using Amazon SageMaker built-in algorithms