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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q224-Q229):
NEW QUESTION # 224
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)
- A. The training channel identifying the location of training data on an Amazon S3 bucket.
- B. The output path specifying where on an Amazon S3 bucket the trained model will persist.
- C. Hyperparameters in a JSON array as documented for the algorithm used.
- D. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
- E. The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
- F. The validation channel identifying the location of validation data on an Amazon S3 bucket.
Answer: A,B,D
NEW QUESTION # 225
A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The Specialist is asked to score a model that was run against the dataset The Specialist has been advised that identifying valid transactions is equally as important as identifying fraudulent transactions What metric is BEST suited to score the model?
- A. Area Under the ROC Curve (AUC)
- B. Root Mean Square Error (RMSE)
- C. Recall
- D. Precision
Answer: A
Explanation:
Explanation
Area Under the ROC Curve (AUC) is a metric that is best suited to score the model for the given scenario.
AUC is a measure of the performance of a binary classifier, such as a model that predicts whether a credit card transaction is valid or fraudulent. AUC is calculated based on the Receiver Operating Characteristic (ROC) curve, which is a plot that shows the trade-off between the true positive rate (TPR) and the false positive rate (FPR) of the classifier as the decision threshold is varied. The TPR, also known as recall or sensitivity, is the proportion of actual positive cases (fraudulent transactions) that are correctly predicted as positive by the classifier. The FPR, also known as the fall-out, is the proportion of actual negative cases (valid transactions) that are incorrectly predicted as positive by the classifier. The ROC curve illustrates how well the classifier can distinguish between the two classes, regardless of the class distribution or the error costs. A perfect classifier would have a TPR of 1 and an FPR of 0 for all thresholds, resulting in a ROC curve that goes from the bottom left to the top left and then to the top right of the plot. A random classifier would have a TPR and an FPR that are equal for all thresholds, resulting in a ROC curve that goes from the bottom left to the top right of the plot along the diagonal line. AUC is the area under the ROC curve, and it ranges from 0 to 1. A higher AUC indicates a better classifier, as it means that the classifier has a higher TPR and a lower FPR for all thresholds. AUC is a useful metric for imbalanced classification problems, such as the credit card transaction dataset, because it is insensitive to the class imbalance and the error costs. AUC can capture the overall performance of the classifier across all possible scenarios, and it can be used to compare different classifiers based on their ROC curves.
The other options are not as suitable as AUC for the given scenario for the following reasons:
Precision: Precision is the proportion of predicted positive cases (fraudulent transactions) that are actually positive. Precision is a useful metric when the cost of a false positive is high, such as in spam detection or medical diagnosis. However, precision is not a good metric for imbalanced classification problems, because it can be misleadingly high when the positive class is rare. For example, a classifier that predicts all transactions as valid would have a precision of 0, but a very high accuracy of 99%.
Precision is also dependent on the decision threshold and the error costs, which may vary for different scenarios.
Recall: Recall is the same as the TPR, and it is the proportion of actual positive cases (fraudulent transactions) that are correctly predicted as positive by the classifier. Recall is a useful metric when the cost of a false negative is high, such as in fraud detection or cancer diagnosis. However, recall is not a good metric for imbalanced classification problems, because it can be misleadingly low when the positive class is rare. For example, a classifier that predicts all transactions as fraudulent would have a recall of 1, but a very low accuracy of 1%. Recall is also dependent on the decision threshold and the error costs, which may vary for different scenarios.
Root Mean Square Error (RMSE): RMSE is a metric that measures the average difference between the predicted and the actual values. RMSE is a useful metric for regression problems, where the goal is to predict a continuous value, such as the price of a house or the temperature of a city. However, RMSE is not a good metric for classification problems, where the goal is to predict a discrete value, such as the class label of a transaction. RMSE is not meaningful for classification problems, because it does not capture the accuracy or the error costs of the predictions.
References:
ROC Curve and AUC
How and When to Use ROC Curves and Precision-Recall Curves for Classification in Python Precision-Recall Root Mean Squared Error
NEW QUESTION # 226
A data engineer at a bank is evaluating a new tabular dataset that includes customer dat a. The data engineer will use the customer data to create a new model to predict customer behavior. After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other.
Which steps should the data engineer take to address this issue? (Choose two.)
- A. Apply min-max feature scaling to the dataset.
- B. Remove a portion of highly correlated features from the dataset.
- C. Apply one-hot encoding category-based variables.
- D. Use a linear-based algorithm to train the model.
- E. Apply principal component analysis (PCA).
Answer: B,E
Explanation:
B) Apply principal component analysis (PCA): PCA is a technique that reduces the dimensionality of a dataset by transforming the original features into a smaller set of new features that capture most of the variance in the data. PCA can help address the issue of multicollinearity, which occurs when some features are highly correlated with each other and can cause problems for some machine learning algorithms. By applying PCA, the data engineer can reduce the number of features and remove the redundancy in the data.
C) Remove a portion of highly correlated features from the dataset: Another way to deal with multicollinearity is to manually remove some of the features that are highly correlated with each other. This can help simplify the model and avoid overfitting. The data engineer can use the correlation matrix to identify the features that have a high correlation coefficient (e.g., above 0.8 or below -0.8) and remove one of them from the dataset. References: = Principal Component Analysis: This is a document from AWS that explains what PCA is, how it works, and how to use it with Amazon SageMaker.
Multicollinearity: This is a document from AWS that describes what multicollinearity is, how to detect it, and how to deal with it.
NEW QUESTION # 227
A data science team is working with a tabular dataset that the team stores in Amazon S3. The team wants to experiment with different feature transformations such as categorical feature encoding. Then the team wants to visualize the resulting distribution of the dataset. After the team finds an appropriate set of feature transformations, the team wants to automate the workflow for feature transformations.
Which solution will meet these requirements with the MOST operational efficiency?
- A. Use AWS Glue Studio with custom code to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualization. Package the feature processing steps into an AWS Lambda function for automation.
- B. Use an Amazon SageMaker notebook instance to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualization. Package the feature processing steps into an AWS Lambda function for automation.
- C. Use Amazon SageMaker Data Wrangler preconfigured transformations to explore feature transformations. Use SageMaker Data Wrangler templates for visualization. Export the feature processing workflow to a SageMaker pipeline for automation.
- D. Use Amazon SageMaker Data Wrangler preconfigured transformations to experiment with different feature transformations. Save the transformations to Amazon S3. Use Amazon QuickSight for visualzation. Package each feature transformation step into a separate AWS Lambda function. Use AWS Step Functions for workflow automation.
Answer: C
Explanation:
The solution A will meet the requirements with the most operational efficiency because it uses Amazon SageMaker Data Wrangler, which is a service that simplifies the process of data preparation and feature engineering for machine learning. The solution A involves the following steps:
Use Amazon SageMaker Data Wrangler preconfigured transformations to explore feature transformations. Amazon SageMaker Data Wrangler provides a visual interface that allows data scientists to apply various transformations to their tabular data, such as encoding categorical features, scaling numerical features, imputing missing values, and more. Amazon SageMaker Data Wrangler also supports custom transformations using Python code or SQL queries1.
Use SageMaker Data Wrangler templates for visualization. Amazon SageMaker Data Wrangler also provides a set of templates that can generate visualizations of the data, such as histograms, scatter plots, box plots, and more. These visualizations can help data scientists to understand the distribution and characteristics of the data, and to compare the effects of different feature transformations1.
Export the feature processing workflow to a SageMaker pipeline for automation. Amazon SageMaker Data Wrangler can export the feature processing workflow as a SageMaker pipeline, which is a service that orchestrates and automates machine learning workflows. A SageMaker pipeline can run the feature processing steps as a preprocessing step, and then feed the output to a training step or an inference step. This can reduce the operational overhead of managing the feature processing workflow and ensure its consistency and reproducibility2.
The other options are not suitable because:
Option B: Using an Amazon SageMaker notebook instance to experiment with different feature transformations, saving the transformations to Amazon S3, using Amazon QuickSight for visualization, and packaging the feature processing steps into an AWS Lambda function for automation will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to write the code for the feature transformations, the data storage, the data visualization, and the Lambda function. Moreover, AWS Lambda has limitations on the execution time, memory size, and package size, which may not be sufficient for complex feature processing tasks3.
Option C: Using AWS Glue Studio with custom code to experiment with different feature transformations, saving the transformations to Amazon S3, using Amazon QuickSight for visualization, and packaging the feature processing steps into an AWS Lambda function for automation will incur more operational overhead than using Amazon SageMaker Data Wrangler. AWS Glue Studio is a visual interface that allows data engineers to create and run extract, transform, and load (ETL) jobs on AWS Glue. However, AWS Glue Studio does not provide preconfigured transformations or templates for feature engineering or data visualization. The data scientist will have to write custom code for these tasks, as well as for the Lambda function. Moreover, AWS Glue Studio is not integrated with SageMaker pipelines, and it may not be optimized for machine learning workflows4.
Option D: Using Amazon SageMaker Data Wrangler preconfigured transformations to experiment with different feature transformations, saving the transformations to Amazon S3, using Amazon QuickSight for visualization, packaging each feature transformation step into a separate AWS Lambda function, and using AWS Step Functions for workflow automation will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to create and manage multiple AWS Lambda functions and AWS Step Functions, which can increase the complexity and cost of the solution. Moreover, AWS Lambda and AWS Step Functions may not be compatible with SageMaker pipelines, and they may not be optimized for machine learning workflows5.
References:
1: Amazon SageMaker Data Wrangler
2: Amazon SageMaker Pipelines
3: AWS Lambda
4: AWS Glue Studio
5: AWS Step Functions
NEW QUESTION # 228
A Machine Learning Specialist wants to determine the appropriate SageMakerVariant Invocations Per Instance setting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS As this is the first deployment, the Specialist intends to set the invocation safety factor to 0 5 Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the sageMakervariantinvocationsPerinstance setting?
Answer: A
NEW QUESTION # 229
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