AIF-C01 Fundamentals of AI and ML Practice Test 6 — 15 Questions

Question 1

A data engineer is using Amazon SageMaker Data Wrangler to prepare tabular data for ML. Which THREE data transformations are natively supported? (Choose three.)

Accepted Answer

One-hot encoding for categorical features. SageMaker Data Wrangler natively supports one-hot encoding as a categorical-encoding transform, which converts categorical features into binary indicator columns suitable for ML models, so option A is correct. It also provides a text vectorization transform using TF-IDF (and other text transforms like bag-of-words and n-gram) to convert text fields into numeric feature vectors, making option C correct. Data Wrangler additionally allows custom transformations through the Custom Transform node, where users can write their own Pandas or PySpark code, so option D is correct. Options B and E are not native Data Wrangler tabular transforms: audio feature extraction and image resizing/normalization are handled by other AWS services or frameworks (for example, SageMaker Processing with librosa or image libraries), not by Data Wrangler's built-in transform list.

Answer

Audio feature extraction

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Image resizing and normalization

Question 2

Refer to the exhibit. A data scientist ran a training job on Amazon SageMaker. The job failed with the error shown. What is the most likely cause?

Accepted Answer

The batch size is too large for the instance's GPU memory. The error message indicates a CUDA out-of-memory error, which occurs when the GPU memory is insufficient for the requested batch size. Option D is correct because increasing the batch size beyond the GPU's memory capacity causes the training job to fail with this specific error.

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The S3 input path is incorrect

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The IAM role does not have permission to access S3

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The training code has a syntax error

Question 3

A company wants to predict customer churn. They have historical data with features like usage minutes, support tickets, contract length. The target is binary: churn/not churn. Which ML algorithm is best suited?

Accepted Answer

Logistic regression. Logistic regression is the best choice because it is specifically designed for binary classification tasks like predicting churn (churn/not churn). It models the probability of the target class using a logistic (sigmoid) function, making it interpretable and efficient for this type of supervised learning problem with a categorical outcome.

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Principal Component Analysis (PCA)

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Linear regression

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K-means clustering

Question 4

A team trained a deep learning model that achieves 99% accuracy on training data but only 70% on validation data. What is the most likely issue?

Accepted Answer

Overfitting. The model performs exceptionally well on training data (99% accuracy) but significantly worse on validation data (70% accuracy). This large gap indicates the model has memorized the training data, including noise and irrelevant patterns, rather than learning generalizable features — a classic symptom of overfitting.

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Underfitting

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Data leakage

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Feature scaling

Question 5

A team has built a regression model to predict house prices. The RMSE is 50,000 on the test set. Which action is most appropriate to improve model performance?

Accepted Answer

Add more relevant features. Adding more relevant features can provide the model with additional predictive signals, potentially reducing bias and lowering RMSE if the new features have genuine correlation with house prices. Since RMSE is already 50,000, the model may be underfitting due to insufficient input variables, and enriching the feature set is a direct way to capture more variance in the target variable.

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Remove outliers from training data

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Apply feature scaling

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Use a different evaluation metric

Question 6

A company is using Amazon Rekognition to detect objects in images. They find that the service sometimes mislabels objects. What is the best way to improve accuracy for their specific use case?

Accepted Answer

Use Amazon SageMaker to build a custom model. Amazon Rekognition is a pre-trained service that may not perform optimally for specialized or domain-specific use cases. By using Amazon SageMaker to build a custom model, you can train a model on your own labeled dataset, which directly addresses the mislabeling issue by tailoring the model to your specific images and objects.

Answer

Use a larger image size

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Contact AWS support

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Increase the confidence threshold

Question 7

A data scientist needs to preprocess categorical data with high cardinality (e.g., zip code with 50,000 unique values). Which technique is most appropriate?

Accepted Answer

Target encoding. Target encoding is the most appropriate technique for high-cardinality categorical features like zip codes because it replaces each category with the mean of the target variable for that category, effectively compressing 50,000 unique values into a single numeric feature while retaining predictive signal. This avoids the dimensionality explosion of one-hot encoding and the arbitrary ordering of label or ordinal encoding, which can mislead models.

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Label encoding

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Ordinal encoding

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One-hot encoding

Question 8

A company is using Amazon Comprehend for sentiment analysis on customer reviews. They notice that the sentiment is often incorrect for negative reviews with sarcasm. What is the likely cause?

Accepted Answer

The pre-trained model cannot handle sarcasm well. Amazon Comprehend's pre-trained sentiment analysis models are trained on general text corpora and lack the ability to detect sarcasm, which relies on contextual cues, tone, and figurative language. Sarcasm often inverts the literal sentiment (e.g., 'Great job, as always' for a failure), and standard NLP models without explicit sarcasm detection or fine-tuning cannot reliably interpret this inversion. Therefore, the likely cause is that the pre-trained model cannot handle sarcasm well.

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The model is not fine-tuned for the domain

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Insufficient training data

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The input text is too long

Question 9

A team is evaluating a classification model. The confusion matrix shows: TP=80, FN=20, FP=10, TN=90. What is the precision?

Accepted Answer

0.89. Precision is calculated as TP / (TP + FP). Here, TP=80 and FP=10, so precision = 80 / (80 + 10) = 80 / 90 = 0.888..., which rounds to 0.89. This metric measures the proportion of positive identifications that were actually correct.

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0.75

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0.80

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0.90

Question 10

Which TWO techniques are commonly used to prevent overfitting in machine learning models? (Select TWO.)

Accepted Answer

Use cross-validation. Option B (Use cross-validation) is correct because techniques like k-fold cross-validation estimate how well a model generalizes to unseen data by training and validating on different data splits, which helps detect and mitigate overfitting during model selection and hyperparameter tuning. Option E (Use regularization) is correct because methods such as L1 (Lasso) and L2 (Ridge) regularization add a penalty term to the loss function that constrains the magnitude of model weights, reducing variance and preventing the model from fitting noise in the training data. The unmarked options do not belong: adding more irrelevant features (A) increases dimensionality and noise, making overfitting worse; increasing model complexity (C) raises variance and typically worsens overfitting; and reducing the amount of training data (D) gives the model less information to learn generalizable patterns, also promoting overfitting.

Answer

Add more irrelevant features

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Increase model complexity

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Reduce the amount of training data

Question 11

A startup needs to predict customer churn based on historical data containing labels (churned or not). Which type of machine learning should they use?

Accepted Answer

Supervised learning. The startup has labeled historical data (churned or not), which is the defining characteristic of supervised learning. The goal is to learn a mapping from input features to the known output labels to predict churn for new customers. This is a classic classification problem, making supervised learning the correct choice.

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Reinforcement learning

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Unsupervised learning

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Semi-supervised learning

Question 12

A data scientist is training a model using Amazon SageMaker and notices the training loss is decreasing but validation loss starts increasing after a few epochs. Which technique should they apply to address this?

Accepted Answer

Add regularization (e.g., L1 or L2). The scenario describes overfitting, where the model memorizes training data but fails to generalize to validation data. Adding regularization (L1 or L2) penalizes large weights, reducing model complexity and improving generalization. This is a standard technique in SageMaker training jobs, often configured via the `regularizer` hyperparameter in frameworks like TensorFlow or MXNet.

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Increase batch size

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Increase the learning rate

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Add more training data

Question 13

A company wants to deploy a real-time inference endpoint for a custom model on SageMaker. The model has high latency (100ms) and they need to handle variable traffic with spikes. Which deployment strategy is most cost-effective?

Accepted Answer

Deploy on a single SageMaker endpoint with automatic scaling. A single SageMaker endpoint with automatic scaling allows the endpoint to dynamically adjust the number of instances based on traffic patterns, handling variable traffic and spikes cost-effectively. For a model with 100ms latency, automatic scaling can add instances during spikes and remove them during low traffic, ensuring you only pay for the compute resources you use while maintaining low inference latency.

Answer

Deploy on a SageMaker multi-model endpoint

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Use batch transform

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Use a single large instance type

Question 14

A company wants to build a model to forecast monthly sales. The data is a time series with trend and seasonality. Which SageMaker algorithm is most appropriate?

Accepted Answer

DeepAR. DeepAR is the most appropriate algorithm because it is specifically designed for time series forecasting, handling both trend and seasonality through autoregressive recurrent neural networks. It learns from multiple related time series and produces probabilistic forecasts, making it ideal for monthly sales prediction.

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XGBoost

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K-Means

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Linear Learner

Question 15

A data scientist is using SageMaker to train a model on a dataset with many features. They suspect some features are redundant. Which feature engineering technique would help?

Accepted Answer

Principal Component Analysis (PCA). Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms the original correlated features into a smaller set of uncorrelated principal components, effectively removing redundancy while preserving most of the variance in the data. In SageMaker, PCA can be applied via the built-in PCA algorithm or as a preprocessing step in a scikit-learn container to reduce feature space and eliminate multicollinearity.

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Feature scaling

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One-hot encoding

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Polynomial features