AIF-C01 Fundamentals of AI and ML — 15 Questions

Question 1

A data scientist wants to quickly build a supervised learning model for binary classification on a tabular dataset with 10,000 rows and 200 features. The dataset has some missing values and requires minimal code. Which AWS service should the data scientist use?

Accepted Answer

Amazon SageMaker Autopilot. Amazon SageMaker Autopilot is the correct choice because it automatically performs data preprocessing (including handling missing values), feature engineering, model selection, and hyperparameter tuning for supervised learning tasks like binary classification. It requires minimal code—users can simply point to a tabular dataset in Amazon S3 and specify the target column, and Autopilot will automatically train and evaluate multiple candidate models, making it ideal for quickly building a binary classifier on a 10,000-row, 200-feature dataset with missing values.

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Amazon SageMaker Studio Lab

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Amazon SageMaker Clarify

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Amazon SageMaker JumpStart

Question 2

An ML team is deploying a real-time inference endpoint for a computer vision model using Amazon SageMaker. The model requires GPU acceleration for low latency. Which instance type should the team choose to minimize cost while meeting the GPU requirement?

Accepted Answer

ml.p3.2xlarge. Option C (ml.p3.2xlarge) is correct because it provides a GPU (NVIDIA Tesla V100) necessary for low-latency GPU acceleration in computer vision inference, while being the most cost-effective GPU instance among the options. The ml.p3.2xlarge offers a single GPU with sufficient compute for real-time inference without over-provisioning resources, minimizing cost compared to larger GPU instances like ml.p4d.24xlarge.

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ml.g5.xlarge

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ml.c5.xlarge

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ml.p4d.24xlarge

Question 3

A company is training a deep learning model on Amazon SageMaker using a custom Docker container. The training job fails with the error 'CannotStartContainerError: API error (500): failed to create shim task'. The team verifies that the container image is compatible with the selected instance type. What is the most likely cause of this error?

Accepted Answer

The instance type does not have enough memory for the container. The error 'CannotStartContainerError: API error (500): failed to create shim task' typically occurs when the Docker container cannot be initialized due to resource constraints, most commonly insufficient memory on the selected instance type. Even if the container image is compatible with the instance, the container's memory request may exceed the available memory, causing the container runtime (containerd) to fail when creating the shim task. This is a known issue in SageMaker when the training job's resource requirements are not aligned with the instance's capacity.

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The training data is stored in the wrong S3 bucket

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The container image does not have the correct entry point

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The GPU drivers are outdated

Question 4

A machine learning engineer is using Amazon SageMaker to train a model and wants to automatically stop the training job if the loss does not improve for 10 consecutive epochs. Which SageMaker feature should be used?

Accepted Answer

SageMaker built-in algorithms with early stopping. Amazon SageMaker built-in algorithms support early stopping, which allows you to automatically terminate a training job when a specified metric, such as loss, stops improving for a defined number of consecutive epochs. This feature is configured directly in the algorithm's hyperparameters (e.g., `early_stopping_patience` for the XGBoost algorithm) and helps save compute time and cost by preventing overfitting.

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SageMaker Training Compiler

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SageMaker Debugger

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SageMaker Experiments

Question 5

A company needs to store large amounts of unstructured training data (images, videos) in a cost-effective manner while ensuring low-latency retrieval for training jobs running on Amazon SageMaker. Which storage solution should be used?

Accepted Answer

Amazon S3. Amazon S3 is the correct choice because it is designed for cost-effective, scalable storage of unstructured data (images, videos) and integrates natively with Amazon SageMaker for low-latency data retrieval during training jobs. S3 provides high throughput and can be accessed directly from SageMaker training instances without the need for file system mounting, making it ideal for large-scale ML workloads.

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Amazon EFS

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Amazon RDS

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Amazon EBS

Question 6

An organization wants to detect anomalies in real-time streaming data from IoT devices. The data includes sensor readings, and the team plans to use a machine learning model. Which AWS service should be used to build and deploy the model with minimal operational overhead?

Accepted Answer

Amazon SageMaker. Amazon SageMaker is the correct choice because it provides a fully managed environment for building, training, and deploying machine learning models at scale. For real-time anomaly detection on streaming IoT data, SageMaker can host a trained model as a real-time endpoint that processes incoming sensor readings via Amazon Kinesis Data Streams or AWS Lambda, minimizing operational overhead by handling infrastructure, scaling, and monitoring automatically.

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AWS Glue

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Amazon QuickSight

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Amazon Kinesis Data Analytics

Question 7

During a SageMaker training job, the data scientist observes that the loss is not decreasing after the initial few epochs. The model is a deep neural network with ReLU activations. Which hyperparameter adjustment is most likely to help?

Accepted Answer

Reduce the learning rate. When loss plateaus after a few epochs with ReLU activations, the model is likely stuck in a region where gradients are small (e.g., near a local minimum or plateau). Reducing the learning rate allows the optimizer to take smaller steps, which can help it navigate out of flat regions and continue decreasing the loss. This is a standard technique to improve convergence when training stalls.

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Increase the number of epochs

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Increase the learning rate

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Decrease the batch size

Question 8

Which TWO services can be used to preprocess data for machine learning in AWS? (Choose two.)

Accepted Answer

AWS Glue. AWS Glue is a fully managed ETL service that can be used to preprocess data for machine learning by cleaning, transforming, and enriching raw data before feeding it into ML models. It provides built-in transforms and can handle both structured and semi-structured data, making it suitable for preparing large datasets for training.

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Amazon Athena

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Amazon Redshift

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AWS Lambda

Question 9

Which THREE statements about Amazon SageMaker Ground Truth are correct? (Choose three.)

Accepted Answer

It provides built-in workflows for image classification and object detection.. Amazon SageMaker Ground Truth provides built-in workflows for common tasks like image classification and object detection, which simplifies the setup of labeling jobs. These pre-built templates handle the UI and data formatting, allowing users to focus on the labeling instructions rather than building the labeling interface from scratch.

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It can only be used for text data.

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It can only use a public workforce from Amazon Mechanical Turk.

Question 10

Which TWO factors should be considered when choosing between a CPU-based instance and a GPU-based instance for training a machine learning model on Amazon SageMaker? (Choose two.)

Accepted Answer

The size of the dataset. Option C is correct because the size of the dataset directly impacts whether a GPU's parallel processing capabilities are beneficial. GPU instances excel at performing many matrix operations simultaneously, which is critical for large datasets where mini-batch gradient descent can be parallelized. For smaller datasets, the overhead of transferring data to GPU memory may negate the performance gains, making CPU instances more cost-effective.

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The number of layers in the model

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The AWS Region

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The choice of hyperparameter optimizer

Question 11

Refer to the exhibit. A data scientist attaches the above IAM policy to a SageMaker notebook instance role. The notebook is in the same AWS account as the S3 bucket. When trying to read a file from 's3://my-bucket/training/data.csv', the data scientist gets an Access Denied error. What is the most likely cause?

Accepted Answer

The policy does not grant 's3:ListBucket' permission. The error occurs because the IAM policy attached to the SageMaker notebook instance role does not include the 's3:ListBucket' permission. When reading a specific object from S3, the SDK first performs a ListObjects (ListBucket) operation to resolve the object key, especially when using high-level APIs like boto3's `read()` or `get_object`. Without this permission, the request is denied even if 's3:GetObject' is allowed.

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The file name contains spaces

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The S3 bucket is in a different Region

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The policy allows 's3:PutObject' which is not needed

Question 12

Refer to the exhibit. A data scientist is training a neural network model on SageMaker. The training log shows the loss values per epoch. Which issue is most likely occurring?

Accepted Answer

The model is overfitting. The training log shows loss values decreasing on the training set but increasing or plateauing on the validation set, which is a classic sign of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training data too well, failing to generalize to unseen data. In SageMaker, monitoring both training and validation loss curves is critical to detect this issue early.

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The number of epochs is insufficient

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The dataset is too small

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The learning rate is too low

Question 13

A data scientist is training a binary classification model to predict customer churn. The dataset has 10,000 records with 9,500 non-churners and 500 churners. After training a logistic regression model, the model achieves 95% accuracy on the test set. However, the business team reports that the model is not useful because it predicts almost all customers as non-churners. Which metric should the data scientist use to evaluate the model's performance in this scenario?

Accepted Answer

Recall. Option D (Recall) is correct because in this highly imbalanced dataset (95% non-churners vs 5% churners), the model's 95% accuracy is misleading—it can achieve this by simply predicting the majority class (non-churner) for all samples. Recall measures the proportion of actual churners correctly identified (True Positives / (True Positives + False Negatives)), directly addressing the business need to detect churn. A high recall ensures the model captures most churners, even at the cost of some false positives.

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Accuracy

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R-squared

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Precision

Question 14

A company is deploying a machine learning model for real-time fraud detection. The model must make predictions with latency under 10 milliseconds. The data scientist trained a gradient boosting model that achieves high accuracy but has inference latency of 50 milliseconds. The team has access to a larger instance type with more CPU cores. Which approach should the data scientist take to reduce inference latency while maintaining accuracy?

Accepted Answer

Deploy the model on a larger instance with more CPU cores.. Option B is correct because increasing the number of CPU cores allows the gradient boosting model to parallelize tree evaluation across multiple cores, reducing inference latency. Since the model is already trained and accurate, this hardware scaling directly addresses the 50 ms bottleneck without altering the model's structure or accuracy.

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Switch to batch inference and run predictions every 100 milliseconds.

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Reduce the maximum tree depth and retrain the model.

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Apply post-training pruning to remove redundant trees.

Question 15

A company wants to build a system that automatically categorizes customer support tickets into predefined categories (e.g., billing, technical, account). The team has a large dataset of historical tickets with their category labels. Which type of machine learning problem is this?

Accepted Answer

Multi-class classification. This is a multi-class classification problem because the model must assign each support ticket to one of three or more predefined categories (e.g., billing, technical, account). The dataset provides labeled historical tickets, making it a supervised learning task, and the output is a discrete class label from a set of more than two categories, which distinguishes it from binary classification.

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Regression

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Binary classification

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Clustering