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The AWS-Certified-Machine-Learning-Specialty Exam covers various topics, such as data engineering, exploratory data analysis, modeling, machine learning implementation and operations, and ethical and legal considerations. Candidates should be well-versed in these topics and should have hands-on experience using AWS services, such as Amazon SageMaker, Amazon S3, Amazon EC2, and Amazon Comprehend.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q304-Q309):

NEW QUESTION # 304
An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.
What combination of services is the MOST efficient to accomplish the task?

• A. Amazon Transcribe, Amazon Translate, and Amazon Comprehend
• B. Amazon Transcribe, Amazon Translate, and Amazon SageMaker BlazingText
• C. Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq
• D. Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)

Answer: D

NEW QUESTION # 305
A Machine Learning Specialist is attempting to build a linear regression model.
Given the displayed residual plot only, what is the MOST likely problem with the model?

• A. Linear regression is appropriate. The residuals have constant variance.
• B. Linear regression is inappropriate. The residuals do not have constant variance.
• C. Linear regression is appropriate. The residuals have a zero mean.
• D. Linear regression is inappropriate. The underlying data has outliers.

Answer: B

Explanation:
A residual plot is a type of plot that displays the values of a predictor variable in a regression model along the x-axis and the values of the residuals along the y-axis. This plot is used to assess whether or not the residuals in a regression model are normally distributed and whether or not they exhibit heteroscedasticity. Heteroscedasticity means that the variance of the residuals is not constant across different values of the predictor variable. This violates one of the assumptions of linear regression and can lead to biased estimates and unreliable predictions. The displayed residual plot shows a clear pattern of heteroscedasticity, as the residuals spread out as the fitted values increase. This indicates that linear regression is inappropriate for this data and a different model should be used. References:
Regression - Amazon Machine Learning
How to Create a Residual Plot by Hand
How to Create a Residual Plot in Python

NEW QUESTION # 306
A financial company is trying to detect credit card fraud. The company observed that, on average, 2% of credit card transactions were fraudulent. A data scientist trained a classifier on a year's worth of credit card transactions data. The model needs to identify the fraudulent transactions (positives) from the regular ones (negatives). The company's goal is to accurately capture as many positives as possible.
Which metrics should the data scientist use to optimize the model? (Choose two.)

• A. Area under the precision-recall curve
• B. Accuracy
• C. True positive rate
• D. False positive rate
• E. Specificity

Answer: A,C

Explanation:
The data scientist should use the area under the precision-recall curve and the true positive rate to optimize the model. These metrics are suitable for imbalanced classification problems, such as credit card fraud detection, where the positive class (fraudulent transactions) is much rarer than the negative class (non-fraudulent transactions).
The area under the precision-recall curve (AUPRC) is a measure of how well the model can identify the positive class among all the predicted positives. Precision is the fraction of predicted positives that are actually positive, and recall is the fraction of actual positives that are correctly predicted. A higher AUPRC means that the model can achieve a higher precision with a higher recall, which is desirable for fraud detection.
The true positive rate (TPR) is another name for recall. It is also known as sensitivity or hit rate. It measures the proportion of actual positives that are correctly identified by the model. A higher TPR means that the model can capture more positives, which is the company's goal.
References:
* Metrics for Imbalanced Classification in Python - Machine Learning Mastery
* Precision-Recall - scikit-learn

NEW QUESTION # 307
A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user.
The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?

• A. Create a FindMatches machine learning transform in AWS Glue.
• B. Create an AWS Glue crawler to infer duplicate accounts in the source data.
• C. Execute the built-in FindDuplicates Amazon Athena query.
• D. Search for duplicate accounts in the AWS Glue Data Catalog.

Answer: A

Explanation:
Explanation/Reference: https://docs.aws.amazon.com/glue/latest/dg/machine-learning.html

NEW QUESTION # 308
A machine learning (ML) developer for an online retailer recently uploaded a sales dataset into Amazon SageMaker Studio. The ML developer wants to obtain importance scores for each feature of the dataset. The ML developer will use the importance scores to feature engineer the dataset.
Which solution will meet this requirement with the LEAST development effort?

• A. Use the multicollinearity feature to perform a lasso feature selection to perform an importance scores analysis.
• B. Use a SageMaker notebook instance to perform principal component analysis (PCA).
• C. Use a SageMaker notebook instance to perform a singular value decomposition analysis.
• D. Use SageMaker Data Wrangler to perform a Gini importance score analysis.

Answer: D

Explanation:
SageMaker Data Wrangler is a feature of SageMaker Studio that provides an end-to-end solution for importing, preparing, transforming, featurizing, and analyzing data. Data Wrangler includes built-in analyses that help generate visualizations and data insights in a few clicks. One of the built-in analyses is the Quick Model visualization, which can be used to quickly evaluate the data and produce importance scores for each feature. A feature importance score indicates how useful a feature is at predicting a target label. The feature importance score is between [0, 1] and a higher number indicates that the feature is more important to the whole dataset. The Quick Model visualization uses a random forest model to calculate the feature importance for each feature using the Gini importance method. This method measures the total reduction in node impurity (a measure of how well a node separates the classes) that is attributed to splitting on a particular feature. The ML developer can use the Quick Model visualization to obtain the importance scores for each feature of the dataset and use them to feature engineer the dataset. This solution requires the least development effort compared to the other options.

NEW QUESTION # 309
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