AIF-C01 AI and ML Fundamentals Practice Test 4 — 15 Questions

Question 1

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 model. By indexing the documents in a vector store and retrieving relevant chunks at inference time, the system can handle monthly updates simply by re-indexing the new documents. This avoids the high cost and latency of retraining or fine-tuning while keeping responses grounded in the current content.

Answer

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

Answer

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

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Fine-tune a base LLM on the policy documents monthly

Question 2

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?

Accepted Answer

AUC-ROC. AUC-ROC is the most appropriate metric because it evaluates the model's ability to distinguish between positive and negative classes across all classification thresholds, making it robust to class imbalance. With only 2% positive instances, accuracy is misleading (98% accuracy can be achieved by simply predicting all negatives), and AUC-ROC captures true positive rate vs. false positive rate trade-offs.

Answer

RMSE

Answer

Accuracy

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MAE

Question 3

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

Accepted Answer

Amazon Textract. Amazon Textract is a machine learning service specifically designed to extract text, handwriting, and structured data (like tables and forms) from scanned documents. It goes beyond simple optical character recognition (OCR) by using ML models to understand the layout and relationships within documents, making it ideal for processing invoices and receipts.

Answer

Amazon Transcribe

Answer

Amazon Comprehend

Answer

Amazon Rekognition

Question 4

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?

Accepted Answer

Implement early stopping to halt training when validation performance degrades. The described behavior—training loss decreasing while validation loss increases—is a classic sign of overfitting. Early stopping monitors the validation loss and halts training when it stops improving (or begins to degrade), preventing the model from memorizing noise in the training data. This directly addresses the overfitting issue without requiring architectural or data changes.

Answer

Add more layers to the neural network

Answer

Increase the amount of training data

Answer

Increase the learning rate

Question 5

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?

Accepted Answer

One-hot encoding. One-hot encoding is the correct technique because it converts each categorical value into a separate binary column (0 or 1), avoiding the false ordinal relationships that linear regression would otherwise assume. For 'Region' (10 values) and 'SubscriptionType' (5 values), one-hot encoding creates 15 binary features, allowing the linear model to learn independent coefficients for each category without implying that 'Region 5' is greater than 'Region 3'.

Answer

Label encoding

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Standardization (z-score normalization)

Answer

Principal component analysis (PCA)

Question 6

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

Accepted Answer

Amazon Personalize. Amazon Personalize is a fully managed machine learning service designed specifically to build personalized recommendation engines (e.g., product recommendations, personalized content) without requiring prior ML expertise. It uses the same technology that powers Amazon.com's recommendations, providing pre-built algorithms and automatic model training, tuning, and deployment via a simple API.

Answer

Amazon SageMaker

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Amazon Rekognition

Answer

Amazon Forecast

Question 7

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?

Accepted Answer

Apply regularization (e.g., L1 or L2). High variance indicates the model is overfitting to the training data, capturing noise rather than the underlying pattern. Regularization (L1/L2) adds a penalty to the loss function for large coefficients, effectively constraining the model complexity and reducing variance, which improves generalization to unseen test data.

Answer

Lower the learning rate

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Increase the number of features

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Decrease the amount of training data

Question 8

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?

Accepted Answer

Use Amazon Rekognition Custom Labels to train a custom model with additional labeled images. Amazon Rekognition Custom Labels allows you to train a custom model using your own labeled dataset, which directly addresses the false positives for a specific category by fine-tuning the model on domain-relevant examples. This improves precision because the base Rekognition model is pre-trained on general data and may not capture the nuances of your specific category, whereas a custom model learns from your labeled images to reduce incorrect detections.

Answer

Increase the confidence threshold to 99%

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Submit a request to AWS to retrain the base Rekognition model

Answer

Use Amazon SageMaker to build a new object detection model from scratch

Question 9

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?

Accepted Answer

Overfitting. The correct answer is B, overfitting. When RMSE on the test set is significantly higher than on the training set, it indicates the model has learned noise and specific patterns from the training data that do not generalize to unseen data. This is the classic symptom of overfitting in linear regression, where the model may have too many features or excessive complexity relative to the data.

Answer

Data leakage from test to training set

Answer

Underfitting

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Insufficient training data

Question 10

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?

Accepted Answer

Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to extract insights from text, including custom classification. It can be trained to automatically categorize support tickets into predefined categories like 'billing', 'technical', and 'account' by analyzing the text content of each ticket.

Answer

Amazon Transcribe

Answer

Amazon Rekognition

Answer

Amazon Textract

Question 11

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

Accepted Answer

Reinforcement learning. Reinforcement learning is correct because it involves an agent learning to make decisions by interacting with an environment, receiving rewards for desirable actions and penalties for undesirable ones. This trial-and-error approach, often modeled using Markov Decision Processes (MDPs), is specifically designed for sequential decision-making tasks like game playing, where the agent must maximize cumulative reward over time.

Answer

Semi-supervised learning

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Supervised learning

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Unsupervised learning

Question 12

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?

Accepted Answer

Enable managed spot training to use EC2 Spot Instances. Managed spot training uses EC2 Spot Instances, which can reduce costs by up to 90% compared to On-Demand instances. SageMaker automatically manages checkpointing and retries for spot interruptions, so model quality is maintained while training time is not negatively impacted. This directly addresses the cost concern without sacrificing performance.

Answer

Decrease the batch size

Answer

Use a larger instance type with more vCPUs

Answer

Increase the number of hyperparameter tuning jobs

Question 13

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.)

Accepted Answer

Imputation of missing values. Imputation of missing values is necessary for categorical features because linear regression cannot handle missing data. Common practice is to impute with the mode. One-hot encoding converts categorical variables into binary vectors, avoiding ordinal bias that label encoding would introduce. These two steps ensure the categorical data is suitable for linear regression.

Answer

Feature scaling

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Principal component analysis (PCA)

Answer

Label encoding

Question 14

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.)

Accepted Answer

Amazon Comprehend. Amazon Comprehend is the only service required for this task because it provides sentiment analysis, key phrase extraction, and entity recognition directly on text data. Amazon Transcribe is for converting audio to text, Amazon Translate is for language translation, and neither is needed since the social media posts are already in text form. Amazon Rekognition handles images/video, and Amazon Personalize generates recommendations. Thus, only Amazon Comprehend is relevant.

Answer

Amazon Transcribe

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Amazon Rekognition

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Amazon Translate

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Amazon Personalize

Question 15

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

Accepted Answer

High bias typically leads to underfitting. High bias typically leads to underfitting because the model is too simple to capture patterns. High variance typically leads to overfitting because the model is too complex and learns noise. Therefore, options C and E are correct. Option A is false because high bias causes underfitting, not overfitting. Option B is false because high variance models are very sensitive to data changes. Option D is false because increasing complexity usually increases variance, not decreases it.

Answer

High bias typically leads to overfitting

Answer

High variance models are insensitive to changes in training data

Answer

Increasing model complexity typically decreases variance