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

Question 1

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. Option A is correct because 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 2

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 3

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 4

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 5

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. Option B is correct because 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 6

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. Option C is correct because 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 7

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 8

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 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

Question 9

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.

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Automated labeling using pre-trained models

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Active learning with manual verification

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Fully automated labeling via AWS Lambda

Question 10

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. Option A is correct because 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.

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Audio feature extraction

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Image resizing and normalization

Question 11

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.

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The S3 input path is incorrect

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The IAM role does not have permission to access S3

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The training code has a syntax error

Question 12

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.

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The bucket policy requires encryption in transit

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The training job is using the wrong AWS region

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The policy does not include the s3:PutObject action

Question 13

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. Option C is correct because 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.

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

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Enable auto-scaling to add instances during high load

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Use SageMaker Model Monitor to detect data drift

Question 14

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.

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Accuracy

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Recall

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Precision

Question 15

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.

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Principal Component Analysis (PCA)

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Linear regression

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K-means clustering