AIF-C01 Fundamentals of AI and ML Practice Test 3 — 15 Questions

Question 1

A company wants to automatically detect anomalies in server metrics. Which algorithm is most appropriate?

Accepted Answer

One-class SVM. One-class SVM is specifically designed for anomaly detection, as it learns a boundary around the normal data points in the feature space and identifies any point falling outside this boundary as an anomaly. This makes it ideal for detecting unusual patterns in server metrics without requiring labeled anomaly examples.

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XGBoost

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Linear SVM

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K-Means

Question 2

A team is training a binary classification model using Amazon SageMaker. They notice that the training accuracy is 99% but the test accuracy is only 70%. Which technique should they apply first to address this?

Accepted Answer

Apply regularization. The high training accuracy (99%) paired with significantly lower test accuracy (70%) is a classic symptom of overfitting, where the model memorizes the training data instead of learning generalizable patterns. Regularization (Option B) is the first-line technique to combat overfitting by adding a penalty to the loss function (e.g., L1 or L2 regularization), which discourages overly complex decision boundaries. In Amazon SageMaker, this can be implemented via hyperparameters like `l1` or `l2` in built-in algorithms or by adding dropout layers in a custom framework.

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Reduce training data

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Increase learning rate

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Increase model complexity

Question 3

During model training, the loss decreases rapidly for the first few epochs and then plateaus. The validation loss starts increasing after some epochs. What should the team do to improve generalization?

Accepted Answer

Early stopping. The validation loss increasing while training loss continues to decrease is a classic sign of overfitting. Early stopping (Option B) halts training when validation performance stops improving, preventing the model from memorizing noise in the training data and thereby improving generalization.

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Increase learning rate

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Increase training epochs

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Add more layers

Question 4

A company is deploying a machine learning model for real-time fraud detection. The model must have latency under 100ms. Which infrastructure choice is most appropriate?

Accepted Answer

Amazon SageMaker real-time endpoints. Amazon SageMaker real-time endpoints are designed for low-latency inference, typically in the tens of milliseconds, making them suitable for real-time fraud detection where latency must be under 100ms. They deploy a model behind a persistent HTTPS endpoint that auto-scales to handle incoming requests with minimal delay.

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Amazon EC2 with Deep Learning AMI

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Amazon SageMaker batch transform

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Amazon SageMaker notebook instance

Question 5

A team trains a model using Amazon SageMaker built-in XGBoost. After training, they want to evaluate feature importance. Which SageMaker feature allows them to view this?

Accepted Answer

SageMaker Debugger. SageMaker Debugger provides built-in monitoring and visualization capabilities, including the ability to capture feature importance metrics (e.g., gain, cover, weight) from XGBoost training jobs. It automatically saves these metrics to Amazon S3 and allows you to view them through the SageMaker Studio Debugger dashboard or by querying the saved tensors, enabling direct evaluation of feature importance without additional custom code.

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SageMaker Experiments

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SageMaker Autopilot

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SageMaker Model Monitor

Question 6

An ML engineer wants to store training data in a format optimized for linear data scanning and columnar access in SageMaker. Which format is most appropriate?

Accepted Answer

Parquet. Parquet is a columnar storage format optimized for both linear data scanning and columnar access, making it ideal for training data in SageMaker. It reduces I/O by storing data by columns rather than rows, enabling efficient retrieval of specific features during model training.

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JSON

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Image (JPEG/PNG)

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CSV

Question 7

A data scientist is preparing data for a classification task. Which TWO techniques are commonly used for handling missing values? (Choose two.)

Accepted Answer

Imputing with mean. Imputing with the mean is a common technique for handling missing values in numerical features because it preserves the overall distribution of the data without reducing the dataset size. This method replaces each missing entry with the arithmetic mean of the non-missing values in that column, which is simple to implement and works well when data is missing completely at random (MCAR).

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Label encoding

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Normalization

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One-hot encoding

Question 8

Which AWS services can be used to build, train, and deploy custom machine learning models? (Choose two.)

Accepted Answer

AWS Deep Learning AMIs. AWS Deep Learning AMIs (C) are pre-configured Amazon Machine Images that include popular deep learning frameworks (TensorFlow, PyTorch, MXNet) and GPU drivers, allowing you to build, train, and deploy custom ML models on EC2 instances. Amazon SageMaker (E) is a fully managed service that provides end-to-end capabilities for building, training, and deploying custom ML models at scale, with built-in algorithms, automatic model tuning, and one-click deployment.

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Amazon Polly

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Amazon Lex

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Amazon Rekognition

Question 9

A company is training a deep learning model for image classification. Which THREE practices help reduce overfitting? (Choose three.)

Accepted Answer

L2 regularization. L2 regularization (also known as weight decay) adds a penalty proportional to the square of the weight magnitudes to the loss function. This discourages the model from learning overly complex patterns by forcing weights to stay small, which reduces overfitting by limiting the model's capacity to fit noise in the training data.

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Increasing model depth

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Increasing learning rate

Question 10

A company wants to automatically detect anomalies in their AWS CloudTrail logs to identify potential security threats. Which AWS service is specifically designed for this purpose?

Accepted Answer

Amazon GuardDuty. Amazon GuardDuty is a threat detection service that continuously monitors AWS accounts and workloads using machine learning, anomaly detection, and integrated threat intelligence. It specifically analyzes CloudTrail management and data events, VPC Flow Logs, and DNS logs to identify unauthorized behavior or potential security threats, making it the correct choice for automatically detecting anomalies in CloudTrail logs.

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Amazon Macie

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AWS Config

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Amazon Inspector

Question 11

A startup is building a recommendation engine for their e-commerce platform. They need a fully managed service that can generate personalized product recommendations based on user behavior. Which AWS service should they use?

Accepted Answer

Amazon Personalize. Amazon Personalize is a fully managed machine learning service specifically designed to generate real-time personalized product recommendations by processing user behavior data (e.g., clicks, purchases, views) and item metadata. It uses the same technology that powers Amazon.com's recommendation engine, making it the correct choice for this e-commerce use case.

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Amazon Rekognition

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Amazon Forecast

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Amazon Comprehend

Question 12

A company uses Amazon SageMaker to train a model. The training job fails with 'InsufficientInstanceCapacity' error. What is the most likely cause?

Accepted Answer

The requested instance type is not available in the specified region.. The 'InsufficientInstanceCapacity' error in Amazon SageMaker indicates that AWS does not currently have enough available capacity for the requested instance type in the specified region or Availability Zone. This is a common transient error when demand for a particular instance type exceeds supply, and it is not related to request rate, dataset size, or image compatibility.

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The request rate is too high.

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The dataset size exceeds the instance storage limit.

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The training image is not compatible with the instance type.

Question 13

A financial services company needs to ensure that the machine learning models used for loan approval are explainable and meet regulatory compliance. Which AWS feature can help explain model predictions?

Accepted Answer

SageMaker Clarify. SageMaker Clarify is the correct AWS service for explaining model predictions because it provides feature attribution and bias detection capabilities. It uses SHAP (SHapley Additive exPlanations) to generate explainability reports, which are essential for meeting regulatory compliance in financial services like loan approval.

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SageMaker Ground Truth

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SageMaker Automatic Model Tuning

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SageMaker Model Monitor

Question 14

A company is using Amazon Rekognition to detect objects in images. They need to detect custom objects that are specific to their domain. What should they do?

Accepted Answer

Use Amazon Rekognition Custom Labels. Amazon Rekognition Custom Labels allows you to train a custom model using your own labeled images to detect domain-specific objects that are not covered by Rekognition's built-in labels. This is the correct service for custom object detection without needing to build a model from scratch.

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Use Amazon Rekognition's built-in labels

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Use Amazon SageMaker Object Detection algorithm

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Use Amazon Comprehend

Question 15

A team is training a deep learning model using Horovod distributed training on SageMaker. They observe that the loss stops decreasing after a few epochs. Which technique should they implement to reduce overfitting?

Accepted Answer

Use dropout regularization. Dropout regularization randomly drops a fraction of neurons during training, which prevents the model from relying too heavily on specific features and forces it to learn more robust representations. This directly addresses overfitting, which is the likely cause of the loss plateauing after a few epochs in a Horovod distributed training setup on SageMaker.

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Increase learning rate

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Add more layers to the model

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Reduce the number of epochs