AIF-C01 Fundamentals of AI and ML Practice Test 5 — 15 Questions

Question 1

A company wants to use Amazon SageMaker Ground Truth to build a labeled dataset for a custom object detection model. Which TWO labeling strategies are available? (Choose two.)

Accepted Answer

Private workforce labeling (company employees). Amazon SageMaker Ground Truth supports private workforce labeling where company employees (e.g., via a corporate directory or invited users) perform manual annotation. This is ideal for sensitive data or domain-specific tasks like custom object detection, where internal expertise ensures high label accuracy.

Answer

Automated labeling using pre-trained models

Answer

Active learning with manual verification

Answer

Fully automated labeling via AWS Lambda

Question 2

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. Amazon SageMaker Data Wrangler includes built-in support for one-hot encoding as a native transformation for categorical features. This transformation automatically creates binary columns for each category, which is essential for preparing tabular data for machine learning models that require numerical input.

Answer

Audio feature extraction

Answer

Image resizing and normalization

Question 3

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.

Answer

The S3 input path is incorrect

Answer

The IAM role does not have permission to access S3

Answer

The training code has a syntax error

Question 4

Refer to the exhibit. A SageMaker training job fails with an 'AccessDenied' error when trying to read files from the S3 bucket 'my-training-data'. The IAM role used by the training job has the policy shown. What is the most likely reason for the failure?

Accepted Answer

The policy does not include the s3:ListBucket action. The IAM policy grants s3:GetObject but not s3:ListBucket. When a SageMaker training job reads files from S3, the SageMaker SDK or framework (e.g., TensorFlow, PyTorch) often performs a ListBucket call first to enumerate objects in the prefix. Without s3:ListBucket, the SDK cannot discover the files, resulting in an AccessDenied error even though GetObject is allowed.

Answer

The bucket policy requires encryption in transit

Answer

The training job is using the wrong AWS region

Answer

The policy does not include the s3:PutObject action

Question 5

Refer to the exhibit. A SageMaker real-time endpoint is experiencing increasing latency and memory errors after running for a few hours. What is the most likely cause and recommended fix?

Accepted Answer

Use SageMaker Debugger to identify and fix a memory leak in the inference code. The symptoms—increasing latency and memory errors after running for a few hours—point to a memory leak in the inference code. SageMaker Debugger can monitor system metrics like memory utilization and detect anomalies, helping to identify the root cause of the leak. Fixing the memory leak directly resolves the progressive degradation, whereas scaling or auto-scaling only masks the symptom.

Answer

Scale the endpoint to a larger instance type, such as ml.r5.large

Answer

Enable auto-scaling to add instances during high load

Answer

Use SageMaker Model Monitor to detect data drift

Question 6

A data scientist is building a binary classification model for fraud detection. The dataset is highly imbalanced (99% legitimate, 1% fraud). Which metric is most appropriate to evaluate model performance?

Accepted Answer

F1-score. In highly imbalanced datasets (99% legitimate, 1% fraud), accuracy is misleading because a model that predicts all transactions as legitimate would achieve 99% accuracy but fail to detect any fraud. The F1-score is the harmonic mean of precision and recall, providing a balanced measure that accounts for both false positives and false negatives, making it the most appropriate metric for evaluating fraud detection models.

Answer

Accuracy

Answer

Recall

Answer

Precision

Question 7

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.

Answer

Principal Component Analysis (PCA)

Answer

Linear regression

Answer

K-means clustering

Question 8

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.

Answer

Underfitting

Answer

Data leakage

Answer

Feature scaling

Question 9

A data scientist is using Amazon SageMaker to train a model. The training job is taking longer than expected. Which change would most likely reduce training time?

Accepted Answer

Use a larger batch size. Using a larger batch size allows the model to process more training samples per iteration, which reduces the number of weight updates needed per epoch and can improve hardware utilization (e.g., GPU parallelism). This often leads to faster training times, provided the batch size fits within memory constraints and does not degrade model convergence.

Answer

Increase the number of training epochs

Answer

Use a smaller instance type

Answer

Enable spot training

Question 10

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

Answer

Contact AWS support

Answer

Increase the confidence threshold

Question 11

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.

Answer

The model is not fine-tuned for the domain

Answer

Insufficient training data

Answer

The input text is too long

Question 12

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.

Answer

0.75

Answer

0.80

Answer

0.90

Question 13

Which TWO of the following are best practices for data preprocessing in machine learning? (Select TWO.)

Accepted Answer

Use cross-validation to evaluate model performance. Cross-validation is a best practice for evaluating model performance because it provides a more robust estimate of how the model will generalize to unseen data by partitioning the data into multiple training and validation sets. This reduces the variance associated with a single train-test split and helps detect overfitting, making it a standard technique in machine learning workflows.

Answer

Always split data 80/20 for training and testing

Answer

One-hot encoding for ordinal categories

Answer

Drop duplicate records only if they are manual entry errors

Question 14

Which THREE of the following are capabilities of Amazon SageMaker? (Select THREE.)

Accepted Answer

Real-time inference endpoints. Amazon SageMaker provides real-time inference endpoints that allow you to deploy trained models to a fully managed HTTPS endpoint for low-latency predictions. These endpoints automatically scale based on traffic and support A/B testing, making them suitable for production workloads.

Answer

On-premises training only

Answer

Can only deploy models to EC2 instances

Question 15

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

Accepted Answer

Use cross-validation. Cross-validation helps prevent overfitting by partitioning the training data into multiple folds, training the model on different subsets, and validating on held-out portions. This provides a more robust estimate of model performance on unseen data and reduces the risk of memorizing noise in a single train-test split.

Answer

Add more irrelevant features

Answer

Increase model complexity

Answer

Reduce the amount of training data