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

Question 1

Refer to the exhibit. A data scientist ran a training job on Amazon SageMaker and it failed. Which action should the data scientist take FIRST to resolve the issue?

Accepted Answer

Use a different instance type that is available in the region. The error indicates that the requested instance type is not available in the current region due to capacity constraints. The first step is to switch to a different instance type that is available in the same region, as this is the quickest and most direct way to resolve the provisioning failure without requiring service limit increases or changing regions.

Answer

Request a service limit increase for the instance type

Answer

Use a different AWS region

Answer

Enable spot training

Question 2

Refer to the exhibit. A developer wants to ensure the notebook instance can access the internet to download packages. Which property configuration ensures this?

Accepted Answer

DirectInternetAccess: Enabled. Setting `DirectInternetAccess: Enabled` on an AWS SageMaker notebook instance allows it to access the internet through a VPC with a Network Address Translation (NAT) gateway or via the public internet if the instance is not in a VPC. This configuration is required to download packages from external repositories like PyPI or conda.

Answer

VolumeSizeInGB: 5

Answer

InstanceType: ml.t2.medium

Answer

The resource type AWS::SageMaker::NotebookInstance

Question 3

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.

Answer

Reinforcement learning

Answer

Unsupervised learning

Answer

Semi-supervised learning

Question 4

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.

Answer

Increase batch size

Answer

Increase the learning rate

Answer

Add more training data

Question 5

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

Answer

Use batch transform

Answer

Use a single large instance type

Question 6

A developer needs to preprocess a dataset consisting of customer reviews for sentiment analysis. Which text preprocessing technique is most likely to improve model accuracy?

Accepted Answer

All of the above. All three listed techniques—stemming, removing stop words, and lowercasing—are standard text preprocessing steps that collectively improve model accuracy for sentiment analysis. Stemming reduces words to root forms to consolidate similar meanings, removing stop words eliminates noise from high-frequency but low-information tokens, and lowercasing normalizes case variations. Together, they reduce the feature space and help the model focus on sentiment-bearing terms, leading to better generalization and accuracy.

Answer

Stemming

Answer

Removing stop words

Answer

Lowercasing

Question 7

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.

Answer

XGBoost

Answer

K-Means

Answer

Linear Learner

Question 8

An organization wants to use Amazon Rekognition to analyze images of people for a security application. They must comply with GDPR. What is the best practice?

Accepted Answer

Ensure all images are anonymized before analysis. GDPR requires that personal data, including facial images, be processed lawfully and with appropriate safeguards. Anonymizing images before analysis with Amazon Rekognition ensures that the data cannot be linked back to an identifiable person, thereby reducing GDPR compliance risk. This aligns with the principle of data minimization and privacy by design.

Answer

Store images indefinitely for audit

Answer

Use celebrity recognition

Answer

Use face detection only

Question 9

In a binary classification problem, the model predicts majority class for all inputs. What is this issue called?

Accepted Answer

High bias. When a model predicts the majority class for all inputs, it indicates that the model is too simplistic and fails to capture the underlying patterns in the data. This is a classic symptom of high bias, where the model makes strong assumptions about the data distribution, leading to systematic underperformance on the minority class. In machine learning, high bias often results from an overly simple algorithm or insufficient model capacity, causing the model to underfit the training data.

Answer

Overfitting

Answer

High variance

Answer

Underfitting

Question 10

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.

Answer

Feature scaling

Answer

One-hot encoding

Answer

Polynomial features

Question 11

A SageMaker endpoint is configured with automatic scaling. The model's inference time is 50ms, and traffic increases gradually. What scaling metric should be used to add instances before latency increases?

Accepted Answer

Invocations per instance. D is correct because 'Invocations per instance' is a custom metric that directly measures the number of inference requests each instance is handling. By setting a target value for this metric, the scaling policy can proactively add instances when the per-instance request count approaches a threshold, preventing latency increases before they occur. This is the recommended approach for SageMaker endpoints with gradual traffic increases, as it anticipates demand rather than reacting to latency spikes.

Answer

Memory utilization

Answer

Concurrent requests

Answer

CPU utilization

Question 12

Which TWO of the following are types of feature scaling?

Accepted Answer

Standardization. Standardization (Z-score scaling) transforms features to have a mean of 0 and a standard deviation of 1, making it a valid type of feature scaling. It is essential when using algorithms that assume normally distributed data, such as linear regression, SVM, or PCA, and it does not bound the data to a fixed range.

Answer

One-hot encoding

Answer

Principal Component Analysis (PCA)

Answer

Binning

Question 13

Which THREE are SageMaker built-in algorithms suitable for regression tasks?

Accepted Answer

Linear Learner. Linear Learner is a SageMaker built-in algorithm that supports both regression and classification tasks. For regression, it models the target variable as a linear combination of input features, optimizing for metrics like mean squared error. It is suitable for regression because it directly outputs continuous values.

Answer

K-Means

Answer

PCA

Question 14

Which TWO are best practices for model monitoring in production on AWS?

Accepted Answer

Monitor input data drift. Monitoring input data drift is a best practice for detecting changes in the distribution of incoming features compared to the training data. This helps identify when the model's assumptions about the data are no longer valid, which can degrade performance. AWS services like Amazon SageMaker Model Monitor can automatically track and alert on data drift.

Answer

Disable logging to reduce latency

Answer

Use only CPU instances

Answer

Retrain model daily

Question 15

A company is training a large language model using Amazon SageMaker. The training job fails with the error 'OutOfMemory'. They are using a single ml.p3.2xlarge instance. The dataset is 50GB and the model is 2GB. The training script uses standard data loading. Which action should they take to resolve the issue?

Accepted Answer

Increase the instance type to ml.p3.16xlarge. The error 'OutOfMemory' indicates that the ml.p3.2xlarge instance (with 16 GB GPU memory) cannot hold both the 2 GB model and the 50 GB dataset during training. Increasing the instance type to ml.p3.16xlarge provides 64 GB GPU memory, which is sufficient to accommodate the model and dataset without memory pressure. This directly resolves the resource constraint.

Answer

Train the model using Spot instances

Answer

Reduce the batch size

Answer

Use SageMaker's Pipe mode for data loading