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

Question 1

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.

Answer

The request rate is too high.

Answer

The dataset size exceeds the instance storage limit.

Answer

The training image is not compatible with the instance type.

Question 2

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.

Answer

SageMaker Ground Truth

Answer

SageMaker Automatic Model Tuning

Answer

SageMaker Model Monitor

Question 3

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.

Answer

Use Amazon Rekognition's built-in labels

Answer

Use Amazon SageMaker Object Detection algorithm

Answer

Use Amazon Comprehend

Question 4

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?

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.

Answer

Random search

Answer

Grid search

Answer

Exhaustive search

Question 5

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.

Answer

Linear regression

Answer

Random forest

Answer

Neural network

Question 6

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 (C) is correct because it is AWS's fully managed NLP service that uses machine learning to extract insights from text, including sentiment analysis, entity recognition, key phrase extraction, language detection, and topic modeling. Amazon Lex (E) is correct because it provides NLP capabilities for building conversational interfaces (chatbots and voice bots) using automatic speech recognition and natural language understanding to interpret user intent and slot values. Amazon Translate (A) is not marked correct because it performs language translation rather than general NLP analysis, even though it is a text-processing service. Amazon Rekognition (B) is not marked correct because it is a computer vision service for image and video analysis, not natural language text. Amazon Polly (D) is not marked correct because it is a text-to-speech service that converts written text into lifelike speech, which is speech synthesis rather than NLP understanding.

Answer

Amazon Translate

Answer

Amazon Rekognition

Answer

Amazon Polly

Question 7

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 is a fully managed ML platform, and three of the listed items are native SageMaker capabilities. B (Notebook instances) is correct because SageMaker provides managed Jupyter notebook instances for data exploration, preprocessing, and model development. C (Ground Truth) is correct because SageMaker Ground Truth is the built-in data labeling service used to create high-quality training datasets, including with automated labeling and human review workflows. D (Model registry) is correct because SageMaker includes a model registry for cataloging, versioning, and managing trained models through approval and deployment stages. A (AWS Glue) is a separate ETL and data catalog service, and E (Amazon Athena) is a separate serverless query service for S3 data, so neither is a component of SageMaker.

Answer

AWS Glue

Answer

Amazon Athena

Question 8

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. Option A is correct because Amazon Fraud Detector's model accuracy improves with more historical event data — a larger volume of labeled fraud/legitimate events gives the training algorithm more examples to learn patterns from, reducing overfitting and improving predictions. Option E is correct because model accuracy depends heavily on feature quality; choosing event variables with stronger predictive power (e.g., customer age, order price, IP address, email domain) directly improves the model's ability to distinguish fraudulent from legitimate transactions. Option B is incorrect because deploying a model to multiple endpoints only affects availability/scalability of predictions, not the underlying model's accuracy. Option C is incorrect because changing the detector type (e.g., online fraud, transaction fraud) alters the use case rather than inherently improving accuracy for the existing scenario. Option D is incorrect because switching to a different model version simply selects an already-trained model; it does not by itself improve accuracy unless retraining with better data or variables occurs.

Answer

Deploy the model to multiple endpoints

Answer

Use a different detector type

Answer

Use a different model version

Question 9

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.

Answer

AWS Lambda

Answer

Amazon EMR

Answer

AWS Glue

Question 10

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.

Answer

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

Answer

Use SageMaker batch transform

Answer

Create a SageMaker asynchronous inference endpoint

Question 11

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.

Answer

SageMaker Model Registry

Answer

SageMaker Debugger

Answer

SageMaker Autopilot

Question 12

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.

Answer

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

Answer

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

Answer

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

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.

Answer

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

Answer

Use SageMaker's data parallelism strategy

Answer

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.

Answer

Use SageMaker Model Monitor to track predictions

Answer

Use SageMaker Experiments to log model parameters

Answer

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. Option B is correct because SageMaker requires model artifacts (the trained TensorFlow SavedModel or model.tar.gz) to be stored in an Amazon S3 bucket, which is then referenced by the SageMaker model when deploying. Option D is correct because deploying to a real-time endpoint requires creating an endpoint configuration that specifies the production variant, instance type, and initial instance count, which is then used to create the endpoint. Option A is not required because the TensorFlow inference container is already provided and maintained by SageMaker as a prebuilt Docker image, so no custom ECR repository is needed unless using a custom container. Option C is not required because the model is already built with TensorFlow; a SageMaker training job is only needed if training within SageMaker. Option E is not required because SageMaker's TensorFlow container supports native TensorFlow SavedModel format, so ONNX conversion is unnecessary.

Answer

Create an Amazon ECR repository for the inference container

Answer

Submit a training job to SageMaker

Answer

Convert the model to ONNX format