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Amazon Web Services MLS-C01 Exam Sample Questions

Question # 1

A company wants to detect credit card fraud. The company has observed that an average of 2% of credit card transactions are fraudulent. A data scientist trains a classifier on a year's worth of credit card transaction data. The classifier needs to identify the fraudulent transactions. The company wants to accurately capture as many fraudulent transactions as possible. Which metrics should the data scientist use to optimize the classifier? (Select TWO.)

A. Specificity

B. False positive rate

C. Accuracy

D. Fl score

E. True positive rate

Answer

D. Fl score

E. True positive rate

Explanation:

The F1 score is a measure of the harmonic mean of precision and recall, which are both important for fraud detection. Precision is the ratio of true positives to all predicted positives, and recall is the ratio of true positives to all actual positives. A high F1 score indicates that the classifier can correctly identify fraudulent transactions and avoid false negatives. The true positive rate is another name for recall, and it measures the proportion of fraudulent transactions that are correctly detected by the classifier. A high true positive rate means that the classifier can capture as many fraudulent transactions as possible.

References:

• Fraud Detection Using Machine Learning | Implementations | AWS Solutions

• Detect fraudulent transactions using machine learning with Amazon SageMaker | AWS

Machine Learning Blog

• 1. Introduction — Reproducible Machine Learning for Credit Card Fraud Detection

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Question # 2

Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

A. Recall

B. Misclassification rate

C. Mean absolute percentage error (MAPE)

D. Area Under the ROC Curve (AUC)

Answer

D. Area Under the ROC Curve (AUC)

Explanation:

Area Under the ROC Curve (AUC) is a metric that measures the performance of a binary classifier across all possible thresholds. It is also known as the probability that a randomly chosen positive example will be ranked higher than a randomly chosen negative example by the classifier. AUC is a good metric to compare different classification models because it is independent of the class distribution and the decision threshold. It also captures both the sensitivity (true positive rate) and the specificity (true negative rate) of the model.

References:

• AWS Machine Learning Specialty Exam Guide

• AWS Machine Learning Specialty Sample Questions

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Question # 3

A machine learning (ML) specialist wants to create a data preparation job that uses a PySpark script with complex window aggregation operations to create data for training and testing. The ML specialist needs to evaluate the impact of the number of features and the sample count on model performance. Which approach should the ML specialist use to determine the ideal data transformations for the model?

A. Add an Amazon SageMaker Debugger hook to the script to capture key metrics. Run the script as an AWS Glue job.

B. Add an Amazon SageMaker Experiments tracker to the script to capture key metrics. Run the script as an AWS Glue job.

C. Add an Amazon SageMaker Debugger hook to the script to capture key parameters. Run the script as a SageMaker processing job.

D. Add an Amazon SageMaker Experiments tracker to the script to capture key parameters. Run the script as a SageMaker processing job.

Answer

D. Add an Amazon SageMaker Experiments tracker to the script to capture key parameters. Run the script as a SageMaker processing job.

Explanation:

Amazon SageMaker Experiments is a service that helps track, compare, and evaluate different iterations of ML models. It can be used to capture key parameters such as the number of features and the sample count from a PySpark script that runs as a SageMaker processing job. A SageMaker processing job is a flexible and scalable way to run data processing workloads on AWS, such as feature engineering, data validation, model evaluation, and model interpretation.

References:

• Amazon SageMaker Experiments

• Process Data and Evaluate Models

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Question # 4

A finance company needs to forecast the price of a commodity. The company has compiled a dataset of historical daily prices. A data scientist must train various forecasting models on 80% of the dataset and must validate the efficacy of those models on the remaining 20% of the dataset. What should the data scientist split the dataset into a training dataset and a validation dataset to compare model performance?

A. Pick a date so that 80% to the data points precede the date Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.

B. Pick a date so that 80% of the data points occur after the date. Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.

C. Starting from the earliest date in the dataset. pick eight data points for the training dataset and two data points for the validation dataset. Repeat this stratified sampling until no data points remain.

D. Sample data points randomly without replacement so that 80% of the data points are in the training dataset. Assign all the remaining data points to the validation dataset.

Answer

A. Pick a date so that 80% to the data points precede the date Assign that group of data points as the training dataset. Assign all the remaining data points to the validation dataset.

Explanation:

A Comprehensive Explanation: The best way to split the dataset into a training dataset and a validation dataset is to pick a date so that 80% of the data points precede the date and assign that group of data points as the training dataset. This method preserves the temporal order of the data and ensures that the validation dataset reflects the most recent trends and patterns in the commodity price. This is important for forecasting models that rely on time series analysis and sequential data. The other methods would either introduce bias or lose information by ignoring the temporal structure of the data.

References:

• Time Series Forecasting - Amazon SageMaker

• Time Series Splitting - scikit-learn

• Time Series Forecasting - Towards Data Science

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Question # 5

Which AWS service can provide a curated selection of pre-trained embedding models to reduce the complexity and cost of vector embeddings?

A. Amazon SageMaker Feature Store

B. Amazon Kendra

C. Amazon SageMaker JumpStart

D. Amazon Comprehend

Answer

C. Amazon SageMaker JumpStart

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