AI and ML Fundamentals

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?

A company is developing a fraud detection system using a neural network. The training loss decreases steadily but the validation loss begins to increase after a certain number of epochs. Which action should be taken to address this issue?

A company is building a model to predict loan default. They have historical data with 5% default rate. The model must minimize false negatives (missed defaults) because each default costs $50,000. False positives (incorrectly flagged defaults) cost $500 in customer service time. The model currently has a recall of 0.70 and precision of 0.80. Which of the following actions would MOST likely reduce the total cost?

A data scientist is preparing a dataset for training a linear regression model. The dataset contains missing values and categorical features. Which TWO actions are appropriate to perform during data preprocessing? (Select TWO.)

A company wants to use a rules-based approach to approve loan applications but finds that it cannot keep up with changing regulations. They have historical data with decisions. Which approach should they adopt?

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?

A financial services company is building an ML model to detect money laundering. The dataset is highly imbalanced (0.1% positive cases). Which THREE techniques can help address the class imbalance? (Select THREE.)

A machine learning practitioner is building a binary classifier for a medical diagnosis application. The cost of a false negative (missing a disease) is very high. Which evaluation metric should the team emphasize?

A company wants to send personalized product recommendations to customers based on their browsing history and previous purchases. Which AWS service is BEST suited for this?

A company needs to convert a large number of recorded customer service calls into text for analysis. Which AWS service should they use?

A company wants to automatically categorize customer support tickets into predefined categories such as 'billing', 'technical', and 'account'. Which AWS service is BEST suited for this task?

A data scientist needs to predict whether a transaction is fraudulent (Yes/No). Which type of machine learning problem is this?

A company wants to identify customer segments based on purchasing behavior. They have unlabeled transaction data and do not know the segment definitions beforehand. Which type of machine learning should they use?

A data science team has trained a gradient boosting model using Amazon SageMaker to predict equipment failures. The model's confusion matrix shows 100 true negatives, 5 false positives, 20 false negatives, and 75 true positives. The cost of a false negative (missed failure) is $10,000, and the cost of a false positive (false alarm) is $500. What is the total cost of the model's predictions on this evaluation set?

A data scientist is training a binary classification model to predict customer churn. After training, the model achieves 95% accuracy on the test set, but the business team reports that the model almost never predicts churn correctly. Which metric should the data scientist focus on to improve the model?

A company uses Amazon Rekognition to detect objects in images. The model is producing a high number of false positives for a specific category. Which action should be taken to improve the model's precision for that category?

A data scientist is building a regression model to predict house prices. During feature engineering, they have categorical variables (e.g., neighborhood) and numerical variables (e.g., square footage) with missing values. Which TWO actions should the data scientist take? (Choose two.)

A data scientist is evaluating a binary classification model's performance. The model was trained on a dataset where 90% of samples belong to class A and 10% to class B. Which THREE metrics are most appropriate to evaluate the model's ability to correctly identify class B? (Choose three.)

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

A team is planning to build an ML model that recommends products to users based on their purchase history. Which AWS service is MOST suitable?

During a binary classification project, the team wants to optimize for correctly identifying positive cases even if it means more false positives. Which metric should they maximize?

A team needs to identify customer segments based on purchasing behavior without predefined categories. Which algorithm should they use?

A company wants to build an ML model to predict customer lifetime value. The dataset includes numerical features (age, income) and categorical features (gender, region). Which TWO preprocessing steps should be applied to the categorical features before training a linear regression model? (Choose TWO.)

A company wants to automatically detect and redact personally identifiable information (PII) from customer support transcripts. Which TWO AWS services can be used together to achieve this? (Choose two.)

A company wants to personalize product recommendations for its e-commerce website. The recommendation engine should adapt to each user's browsing and purchase history in real time. Which AWS service is MOST suitable?

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.)

A machine learning engineer needs to choose a service to extract text from scanned PDF forms, including handwritten fields. Which AWS service is MOST appropriate?

A machine learning engineer notices that the training loss decreases steadily, but the validation loss starts increasing after a few epochs. Which of the following is the MOST likely cause?

A machine learning engineer is training a neural network and wants to prevent overfitting. Which technique should they apply?

Which AWS service is best suited for extracting text (including handwriting) from scanned documents, such as invoices and forms?

Which AWS service can be used to create a personalized recommendation engine for an e-commerce website without requiring prior machine learning experience?

A retail company wants to use Amazon SageMaker to build a model that forecasts product demand. The data includes historical sales, promotions, and holidays. Which THREE actions should the company take to use Amazon Forecast effectively? (Choose three.)

A company has a large dataset of customer support emails labeled with issue categories. They need to classify new emails automatically. Which algorithm is BEST suited for this task?

A machine learning team is working on a multi-label classification problem. They have a highly imbalanced dataset where some labels appear very infrequently. Which evaluation metric is MOST appropriate for assessing model performance across all labels?

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?

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?

An ML engineer is training a linear regression model and notices that adding more features increases training error. What is the most likely cause?

A machine learning engineer normalizes numerical features to have mean 0 and standard deviation 1 before training a linear regression model. What is the PRIMARY benefit of this normalization?

Which of the following is a supervised learning algorithm used for classification tasks?

Which of the following is a benefit of using cross-validation during model training?

A machine learning team is building a model to predict house prices. They have a dataset with 50 features, including square footage, number of bedrooms, and neighborhood. They want to reduce overfitting and improve model interpretability. Which technique should they apply?

A company wants to automatically categorize customer feedback emails into topics such as 'billing', 'technical support', and 'general inquiry'. Which AWS service is MOST appropriate?

Which AWS service can be used to extract text and data from scanned documents such as invoices and receipts?

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?

A data scientist trains a binary classifier and obtains the following confusion matrix on the test set: TP=120, FN=30, FP=40, TN=210. What is the F1 score?

A team is building a deep learning model for image segmentation using Amazon SageMaker. They want to improve the model's generalization and reduce overfitting. Which THREE techniques should they apply? (Select THREE.)

An ML team is preparing a dataset for a classification task. Which THREE data preprocessing steps should they perform? (Select THREE.)

A data scientist is building a binary classifier to predict customer churn. The dataset is highly imbalanced (95% non-churn, 5% churn). Which metric should be prioritized for model evaluation?

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?

A data scientist is fine-tuning a large language model (LLM) using Amazon SageMaker. The training job is taking a long time and the cost is higher than expected. Which configuration change would MOST effectively reduce training time and cost while maintaining model quality?

A company wants to extract key phrases and sentiment from customer reviews. Which TWO AWS services can be combined to accomplish this? (Select TWO.)

A retail company wants to forecast product demand at the SKU level for the next 12 weeks. Which AWS service is purpose-built for this task?

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?

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?

An AWS AI practitioner is designing a document processing pipeline using Amazon Textract and Amazon Comprehend. The pipeline must extract text from PDFs, detect entities, and classify documents into categories (e.g., invoice, contract, report). Which THREE steps should be included in the pipeline? (Choose three.)

Which type of machine learning is used when a model learns to play a game by receiving rewards or penalties for its actions?

An ML team notices that the training accuracy is 99% but validation accuracy is only 72%. Which concept best describes this situation?

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

A company needs to analyze customer feedback from social media posts (text) and determine the sentiment (positive, negative, neutral). They also need to extract key phrases and entities mentioned in the posts. Which THREE AWS services can be combined to accomplish this? (Choose THREE.)

A machine learning team is building a binary classifier using Amazon SageMaker. The dataset has 10,000 features and 1,000 samples. The model overfits severely. Which TWO approaches are MOST likely to reduce overfitting? (Choose two.)

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?

A team is using Amazon SageMaker to train a deep learning model. The training job is taking too long. Which action is MOST likely to reduce training time while maintaining model quality?

A team is building a real-time anomaly detection system for IoT sensor data. The data is unlabeled, and the team expects the anomalies to be rare but of high importance. Which combination of approach and AWS service should the team use?

A machine learning practitioner is training a model to forecast product demand and observes that the model performs well on training data but poorly on unseen data. Which of the following is the MOST likely cause?

A security team needs to detect anomalies in AWS CloudTrail logs to identify potential unauthorized access. They want to use machine learning without manually labeling data or training custom models. Which AWS service should they use?

A machine learning engineer is using Amazon SageMaker to deploy a real-time inference endpoint for a classification model. The model must provide low-latency predictions and handle variable traffic. Which THREE actions should the engineer take? (Select THREE.)

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?

Which AWS service can be used to convert a recorded speech file into text for further analysis?

A company wants to extract text and data from scanned PDF invoices for automated processing. Which AWS service is MOST appropriate for this task?

Which TWO statements about the bias-variance tradeoff are correct? (Choose TWO.)

During model training, a data scientist notices that the model performs very well on the training data but poorly on the test data. The scientist suspects high variance. Which technique is MOST likely to reduce the variance and improve test performance?

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?

A company needs to build a system that converts text-based customer reviews into audio files for accessibility. Which TWO AWS services should be used? (Choose two.)

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

A company is deploying a machine learning model to detect fraudulent transactions. The dataset is highly imbalanced (1% fraud). The team needs to evaluate model performance and minimize false positives while maintaining high recall. Which TWO metrics should they focus on? (Select TWO.)

A data scientist is evaluating a logistic regression model for a binary classification task. The model's AUC-ROC score is 0.95 on the training set and 0.51 on the test set. What is the MOST likely issue?

A data scientist is building a model to predict housing prices. The dataset contains features such as square footage, number of bedrooms, and location. After training a linear regression model, the RMSE on the test set is significantly higher than on the training set. What is the MOST likely cause?

A data scientist trains a linear regression model to predict housing prices. The model achieves a low training error but a high test error. Which concept does this BEST illustrate?

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?

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?

Which TWO of the following are types of unsupervised learning? (Select TWO.)

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?

A data scientist has trained a random forest model that achieves 92% accuracy on the training set but only 75% on the test set. The dataset has 1000 samples and 20 features. Which THREE actions could help improve the model's generalization? (Select THREE.)

An e-commerce company uses Amazon Personalize to provide product recommendations. The business team observes that the recommendations are dominated by popular items and rarely suggest niche products, even for users with long purchase histories. Which Personalize recipe or configuration change would BEST address this issue?

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

A data scientist observes that a gradient boosting model's performance on the validation set is significantly worse than on the training set. Which adjustment is MOST likely to reduce this gap?

A data scientist is using Amazon SageMaker to train a deep learning model. The training job fails with a 'ResourceLimitExceeded' error. What is the MOST likely cause of this error?

Which AWS service can extract text from scanned PDF documents, including handwriting and checkboxes?

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?

A company needs to convert live customer support calls into text for analysis and search. Which TWO AWS services should be used together? (Select TWO.)

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.)

A data scientist is training a binary classification model on a highly imbalanced dataset where the positive class represents only 2% of the data. The model currently achieves 98% accuracy but fails to identify any positive instances. Which metric is MOST appropriate to evaluate this model's performance?

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?

A team is using Amazon SageMaker to build a text classification model. They have raw text data in CSV files stored in Amazon S3. Before training, they need to perform feature engineering. Which THREE actions should they take? (Select THREE.)

A team trained a binary classifier to detect fraudulent transactions. The dataset is highly imbalanced (1% fraud). The model achieves 99% accuracy but only catches 5% of actual fraud cases. Which metric should the team primarily optimize?

A machine learning engineer needs to train a model to predict customer churn. The dataset includes categorical features such as 'Region' (10 possible values) and 'SubscriptionType' (5 possible values). Which feature engineering technique should be used to convert these categorical features into numeric form for a linear regression model?

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?

A machine learning engineer is training a regression model using Amazon SageMaker. The training loss decreases steadily, but the validation loss starts increasing after 20 epochs. Which action should the engineer take to address this issue?

A company wants to build a system that automatically routes support tickets to the appropriate department based on the ticket text. The system must handle new categories that emerge over time without retraining. Which TWO approaches should the company combine to achieve this? (Select TWO.)

Which of the following is an example of reinforcement learning?