AIF-C01 Fundamentals of AI and ML — Timed 10 Questions

Question 1

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?

10 minute time limit — choose an answer to begin.

Accepted Answer

ml.g5.xlarge. (ml.g5.xlarge) is correct because it provides a GPU (NVIDIA A10G Tensor Core GPU) necessary for low-latency GPU acceleration in computer vision inference, while being the most cost-effective GPU instance among the options. The ml.g5.xlarge offers sufficient GPU compute for real-time inference at a lower hourly cost compared to ml.p3.2xlarge, making it the optimal choice for minimizing cost while meeting the GPU requirement.

Answer

ml.c5.xlarge

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ml.p3.2xlarge

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ml.p4d.24xlarge

Question 2

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The GPU drivers are outdated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 3

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 4

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 S3 bucket is in a different Region. The Access Denied error is most likely due to the S3 bucket being in a different Region. While IAM policies are global, bucket policies can include conditions that restrict access based on the request's Region. If the bucket policy denies requests from the Region where the SageMaker notebook is running, the read operation fails. The other options are incorrect: file name spaces cause a different error (NoSuchKey), s3:ListBucket is not required for direct object reads with GetObject, and allowing s3:PutObject does not cause Access Denied for reads.

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The file name contains spaces

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The policy does not grant 's3:ListBucket' permission

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

Question 5

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 6

A financial services company uses a machine learning model to approve loan applications. The model is a gradient boosting classifier trained on historical loan data. Recently, the company noticed that the model's approval rate for applicants from a certain demographic group is significantly lower than for other groups, even though the model's overall accuracy remains high. The data science team has been asked to address this potential bias while minimizing the impact on overall model performance. The team has access to the training data and the trained model. They have limited time and budget. Which course of action should the team take first?

Accepted Answer

Analyze the training data for bias and retrain the model using bias mitigation techniques such as reweighting.. The first step should be to analyze the training data for bias, as biases often originate from historical imbalances or labeling issues. Once bias is identified, techniques like reweighting can be applied during retraining to mitigate bias with minimal impact on overall performance. Option A (removing the sensitive attribute) is insufficient because other features may correlate with the sensitive attribute, and the model can still learn biased patterns. Option B (collecting more data from the under-represented group) is resource-intensive and may not be feasible within limited time and budget; also, it does not address bias in the existing data. Option D (adjusting threshold after deployment) is a post-hoc fix that can lead to inconsistent decisions and may not address the root cause; it is better to address bias during training.

Answer

Remove the sensitive attribute from the training data and retrain the model.

Answer

Collect more data from the under-represented demographic group and retrain the model.

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Adjust the model's decision threshold for the affected group after deployment.

Question 7

A retail company uses a machine learning model to forecast daily product demand. The model is a time series model that uses historical sales data. The model has been performing well, but recently the forecasts have been consistently too low, leading to stockouts. The data scientist notices that the model was trained on data up to last year, and the company has since launched a successful marketing campaign that increased sales by 20%. The data scientist needs to update the model to reflect the new sales patterns. Which approach should the data scientist take?

Accepted Answer

Retrain the model on the most recent data that includes the sales from the marketing campaign.. Retraining the model on the most recent data that includes the sales from the marketing campaign allows the model to learn the new underlying pattern in the time series. Since the model is a time series model, it relies on historical patterns to make forecasts; retraining on data that captures the 20% sales lift ensures the model adapts to the new demand level, reducing the persistent underforecasting.

Answer

Add a feature for the marketing campaign and continue using the old model.

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Switch to a different model type, such as ARIMA, without retraining.

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Multiply the model's predictions by 1.2 to account for the marketing campaign.

Question 8

A data scientist is preparing data for a machine learning model. What is the purpose of splitting the data into training, validation, and test sets?

Accepted Answer

To evaluate model generalization. The test set is held out entirely from the training process and is used only once at the end to evaluate the model's performance on unseen data. This provides an unbiased estimate of how well the model generalizes to new, real-world data, which is the ultimate goal of supervised learning.

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To tune hyperparameters

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To balance class distributions

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To prevent overfitting during training

Question 9

A data scientist is working with a dataset that contains both numerical and categorical features. Which algorithm is commonly used for regression tasks in AWS SageMaker?

Accepted Answer

Linear Learner. Linear Learner (option B) is the correct choice because it is a supervised learning algorithm in AWS SageMaker specifically designed for both regression and classification tasks. It can handle datasets with mixed numerical and categorical features (after appropriate encoding) and provides built-in mechanisms for training linear models, including automatic model tuning and distributed training. Note that option D is a duplicate of option B and is not considered the correct answer in this context.

Answer

K-Means

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BlazingText

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Linear Learner

Question 10

A team has built a regression model to predict house prices. The RMSE is 50,000 on the test set. Which action is most appropriate to improve model performance?

Accepted Answer

Add more relevant features. Adding more relevant features can provide the model with additional predictive signals, potentially reducing bias and lowering RMSE if the new features have genuine correlation with house prices. Since RMSE is already 50,000, the model may be underfitting due to insufficient input variables, and enriching the feature set is a direct way to capture more variance in the target variable.

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Remove outliers from training data

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Apply feature scaling

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Use a different evaluation metric