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

Question 1

A data scientist wants to perform automatic model tuning (hyperparameter optimization) on SageMaker. They need to find the best hyperparameters for a gradient boosting model. Which strategy is BEST for this task?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Bayesian optimization. Bayesian optimization is the best strategy for automatic model tuning on SageMaker because it builds a probabilistic model of the objective function and uses it to select the most promising hyperparameters to evaluate next. This approach is far more sample-efficient than random or grid search, making it ideal for expensive-to-evaluate models like gradient boosting, where each training run consumes significant time and compute resources.

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Random search

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Grid search

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Exhaustive search

Question 2

An e-commerce company stores user interaction logs in Amazon S3. They want to use machine learning to segment users based on purchasing behavior. Which unsupervised learning algorithm is most appropriate?

Accepted Answer

K-means clustering. K-means clustering is the most appropriate unsupervised learning algorithm for segmenting users based on purchasing behavior because it groups data points into clusters based on feature similarity without requiring labeled training data. The e-commerce scenario involves discovering natural groupings (segments) in user interaction logs, which is a classic clustering task, and K-means efficiently partitions users into K distinct segments by minimizing within-cluster variance.

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Linear regression

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Random forest

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Neural network

Question 3

A company wants to use AWS services to process natural language text. Which TWO AWS services provide natural language processing (NLP) capabilities? (Select TWO.)

Accepted Answer

Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to extract insights such as entities, key phrases, sentiment, and syntax from text. Amazon Lex provides NLP capabilities through automatic speech recognition (ASR) and natural language understanding (NLU) to build conversational interfaces like chatbots.

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Amazon Translate

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Amazon Rekognition

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Amazon Polly

Question 4

A data scientist is evaluating different AWS services for building a machine learning pipeline. Which THREE components are part of Amazon SageMaker? (Select THREE.)

Accepted Answer

Notebook instances. Amazon SageMaker provides fully managed notebook instances that allow data scientists to spin up Jupyter notebooks for data exploration, preprocessing, and model development without managing underlying infrastructure. These instances come pre-installed with common ML frameworks and can be easily scaled.

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AWS Glue

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Amazon Athena

Question 5

A company is using Amazon Fraud Detector to detect fraudulent transactions. Which TWO actions can be taken to improve model accuracy? (Select TWO.)

Accepted Answer

Increase the volume of event data. Increasing the volume of event data provides Amazon Fraud Detector with more examples of both fraudulent and legitimate transactions, which allows the model to learn more robust patterns and reduce overfitting. More data helps the model generalize better to unseen events, directly improving prediction accuracy.

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Deploy the model to multiple endpoints

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Use a different detector type

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Use a different model version

Question 6

A startup with limited ML expertise wants to quickly prototype a binary classification model using a small customer dataset. They need a managed environment to run Jupyter notebooks and access pre-built algorithms. Which AWS service should they choose?

Accepted Answer

Amazon SageMaker. Amazon SageMaker is the correct choice because it provides a fully managed environment for Jupyter notebooks and includes built-in, pre-built algorithms for binary classification. This allows the startup to quickly prototype without deep ML expertise, as SageMaker handles infrastructure, scaling, and model training.

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AWS Lambda

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Amazon EMR

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AWS Glue

Question 7

A data scientist wants to host a pre-trained model on Amazon SageMaker for real-time inference with minimal latency. Which approach should they use?

Accepted Answer

Deploy the model on a SageMaker real-time endpoint. SageMaker real-time endpoints are designed for low-latency, synchronous inference. They keep the model loaded and ready to respond to individual requests, making them ideal for real-time applications where minimal latency is critical.

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Run inference using AWS Lambda with the model packaged as a container

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Use SageMaker batch transform

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Create a SageMaker asynchronous inference endpoint

Question 8

A company is using Amazon SageMaker to train a model. They want to automatically stop training if the model performance stops improving on a validation dataset. Which SageMaker feature should they enable?

Accepted Answer

Early stopping in hyperparameter tuning. Amazon SageMaker's hyperparameter tuning jobs support an 'early stopping' feature that automatically halts training when the model's performance on the validation dataset ceases to improve. This is enabled by setting the `EarlyStoppingType` parameter to `Auto` or `Off` in the tuning job configuration, which uses algorithms like median stopping or Bayesian optimization to detect convergence and prevent wasted compute.

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SageMaker Experiments

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SageMaker Debugger

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SageMaker Model Monitor

Question 9

A company is training a deep learning model on Amazon SageMaker using a large dataset stored in S3. Training jobs are frequently failing with 'OutOfMemoryError'. The training algorithm uses PyTorch. How should the data scientist solve this without reducing model accuracy?

Accepted Answer

Use SageMaker Pipe mode for data ingestion. SageMaker Pipe mode streams training data directly from S3 into the algorithm without first downloading it to the local disk, which drastically reduces memory consumption. This allows the model to handle large datasets that would otherwise cause an OutOfMemoryError when using the default File mode, all while preserving the original model architecture and accuracy.

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Reduce the number of layers in the model

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Increase the batch size

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Use a smaller instance type with less memory

Question 10

A data science team is using Amazon SageMaker to train multiple models with different hyperparameters. They want to track metrics, compare runs, and reproduce the best result. Which SageMaker feature should they use?

Accepted Answer

SageMaker Experiments. SageMaker Experiments is the correct feature because it is specifically designed to track, organize, and compare machine learning training runs (trials) with different hyperparameters and metrics. It allows data scientists to log parameters, metrics, and artifacts for each run, compare results across runs, and retrieve the exact configuration needed to reproduce the best-performing model.

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SageMaker Model Registry

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SageMaker Debugger

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SageMaker Autopilot

Question 11

A company wants to use Amazon SageMaker to train a model using a custom Docker container that has specific dependencies. The training code is stored in an S3 bucket. Which steps must be taken to run the training job?

Accepted Answer

Push the custom container to Amazon ECR and create a training job with the container URI. Amazon SageMaker requires custom Docker containers to be stored in Amazon Elastic Container Registry (ECR) to run training jobs. The container URI from ECR is specified in the `AlgorithmSpecification` parameter of the `CreateTrainingJob` API call, allowing SageMaker to pull and execute the container with the training code from S3. Option B correctly describes this mandatory workflow.

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Install dependencies via SageMaker's lifecycle configuration instead of a custom container

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Use SageMaker's built-in framework container and override the entry point

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Upload the container to S3 and reference it in the training job

Question 12

A financial services company needs to deploy a real-time fraud detection model with sub-100ms inference latency. The model is a large ensemble requiring 8 GB of memory per request. The workload has bursty traffic. Which Amazon SageMaker deployment strategy best meets these requirements?

Accepted Answer

Use a single ml.r5.2xlarge instance with an auto-scaling policy based on CPU utilization. A single ml.r5.2xlarge instance provides 16 GB of memory, which can handle the 8 GB per request requirement, and SageMaker real-time endpoints with auto-scaling based on CPU utilization can dynamically adjust to bursty traffic while maintaining sub-100ms inference latency. This approach avoids the overhead of load balancers or multi-model caching that could introduce latency.

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Deploy behind an Application Load Balancer with multiple ml.m5.xlarge EC2 instances running the model

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Use a SageMaker multi-model endpoint with ml.m5.large instances to cache multiple models

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Use SageMaker asynchronous inference with a large batch size

Question 13

A company is using Amazon SageMaker to train a large language model with hundreds of billions of parameters. The model does not fit into the memory of a single GPU. Which approach should they use to train the model efficiently?

Accepted Answer

Use SageMaker's model parallelism strategy with the SageMaker distributed training library. SageMaker's model parallelism strategy with the SageMaker distributed training library is specifically designed for training large models that do not fit into the memory of a single GPU. It partitions the model layers across multiple GPUs, enabling efficient training of models with hundreds of billions of parameters by overlapping computation and communication.

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Use a larger instance with more GPU memory, such as p4d.24xlarge

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Use SageMaker's data parallelism strategy

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Reduce the model size by pruning layers until it fits into memory

Question 14

A healthcare company is using Amazon SageMaker to deploy a model that makes predictions on patient data. They need to ensure that the model's predictions are explainable to comply with regulations. Which approach should they take?

Accepted Answer

Use SageMaker Clarify to generate feature importance and explanations. SageMaker Clarify is specifically designed to provide model explainability, including feature importance and SHAP-based explanations, which are essential for regulatory compliance in healthcare. It helps stakeholders understand why a model made a particular prediction, addressing transparency requirements.

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Use SageMaker Model Monitor to track predictions

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Use SageMaker Experiments to log model parameters

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Use SageMaker Debugger to analyze training gradients

Question 15

A data scientist wants to deploy a custom model built with TensorFlow to Amazon SageMaker for real-time inference. Which TWO steps are required? (Choose two.)

Accepted Answer

Upload the model artifacts to an S3 bucket. SageMaker requires model artifacts (the trained model files) to be stored in an S3 bucket before they can be used for inference. When deploying a custom TensorFlow model, you must upload the saved model (e.g., in SavedModel format) to S3, and then SageMaker will download it to the inference container during endpoint creation.

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Create an Amazon ECR repository for the inference container

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Submit a training job to SageMaker

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Convert the model to ONNX format