MLS-C01 Latest Exam Materials & MLS-C01 Authentic Exam Questions

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q11-Q16):

NEW QUESTION # 11
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. Fl score
B. Specificity
C. False positive rate
D. True positive rate
E. Accuracy

Answer: A,D

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

NEW QUESTION # 12
A data scientist obtains a tabular dataset that contains 150 correlated features with different ranges to build a regression model. The data scientist needs to achieve more efficient model training by implementing a solution that minimizes impact on the model's performance. The data scientist decides to perform a principal component analysis (PCA) preprocessing step to reduce the number of features to a smaller set of independent features before the data scientist uses the new features in the regression model.
Which preprocessing step will meet these requirements?

A. Reduce the dimensionality of the dataset by removing the features that have the highest correlation Load the data into Amazon SageMaker Data Wrangler Perform a Standard Scaler transformation step to scale the data Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data
B. Reduce the dimensionality of the dataset by removing the features that have the lowest correlation. Load the data into Amazon SageMaker Data Wrangler. Perform a Min Max Scaler transformation step to scale the data. Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.
C. Use the Amazon SageMaker built-in algorithm for PCA on the dataset to transform the data
D. Load the data into Amazon SageMaker Data Wrangler. Scale the data with a Min Max Scaler transformation step Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.

Answer: D

Explanation:
Principal component analysis (PCA) is a technique for reducing the dimensionality of datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. PCA is useful when dealing with datasets that have a large number of correlated features. However, PCA is sensitive to the scale of the features, so it is important to standardize or normalize the data before applying PCA. Amazon SageMaker provides a built-in algorithm for PCA that can be used to transform the data into a lower-dimensional representation. Amazon SageMaker Data Wrangler is a tool that allows data scientists to visually explore, clean, and prepare data for machine learning. Data Wrangler provides various transformation steps that can be applied to the data, such as scaling, encoding, imputing, etc. Data Wrangler also integrates with SageMaker built-in algorithms, such as PCA, to enable feature engineering and dimensionality reduction. Therefore, option B is the correct answer, as it involves scaling the data with a Min Max Scaler transformation step, which rescales the data to a range of [0, 1], and then using the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data. Option A is incorrect, as it does not involve scaling the data before applying PCA, which can affect the results of the dimensionality reduction. Option C is incorrect, as it involves removing the features that have the highest correlation, which can lead to information loss and reduce the performance of the regression model. Option D is incorrect, as it involves removing the features that have the lowest correlation, which can also lead to information loss and reduce the performance of the regression model. References:
Principal Component Analysis (PCA) - Amazon SageMaker
Scale data with a Min Max Scaler - Amazon SageMaker Data Wrangler
Use Amazon SageMaker built-in algorithms - Amazon SageMaker Data Wrangler

NEW QUESTION # 13
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information, which model would have the HIGHEST accuracy?

A. Long short-term memory (LSTM) model with scaled exponential linear unit (SELU)
B. Logistic regression
C. Single perceptron with tanh activation function
D. Support vector machine (SVM) with non-linear kernel

Answer: B

Explanation:
Explanation/Reference: https://machinelearningmastery.com/logistic-regression-for-machine-learning/

NEW QUESTION # 14
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers Currently, the company has the following data in Amazon Aurora
* Profiles for all past and existing customers
* Profiles for all past and existing insured pets
* Policy-level information
* Premiums received
* Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?

A. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
B. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
C. Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
D. Use clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

Answer: C

NEW QUESTION # 15
A large company has developed a B1 application that generates reports and dashboards using data collected from various operational metrics The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports The company wants the executives to be able ask questions using written and spoken interlaces Which combination of services can be used to build this conversational interface? (Select THREE)

A. Alexa for Business
B. Amazon Comprehend
C. Amazon Connect
D. Amazon Poly
E. Amazon Transcribe
F. Amazon Lex

Answer: B,E,F

Explanation:
To build a conversational interface that can use natural language to get data from the reports, the company can use a combination of services that can handle both written and spoken inputs, understand the user's intent and query, and extract the relevant information from the reports. The services that can be used for this purpose are:
Amazon Lex: A service for building conversational interfaces into any application using voice and text. Amazon Lex can create chatbots that can interact with users using natural language, and integrate with other AWS services such as Amazon Connect, Amazon Comprehend, and Amazon Transcribe. Amazon Lex can also use lambda functions to implement the business logic and fulfill the user's requests.
Amazon Comprehend: A service for natural language processing and text analytics. Amazon Comprehend can analyze text and speech inputs and extract insights such as entities, key phrases, sentiment, syntax, and topics. Amazon Comprehend can also use custom classifiers and entity recognizers to identify specific terms and concepts that are relevant to the domain of the reports.
Amazon Transcribe: A service for speech-to-text conversion. Amazon Transcribe can transcribe audio inputs into text outputs, and add punctuation and formatting. Amazon Transcribe can also use custom vocabularies and language models to improve the accuracy and quality of the transcription for the specific domain of the reports.
Therefore, the company can use the following architecture to build the conversational interface:
Use Amazon Lex to create a chatbot that can accept both written and spoken inputs from the executives. The chatbot can use intents, utterances, and slots to capture the user's query and parameters, such as the report name, date, metric, or filter.
Use Amazon Transcribe to convert the spoken inputs into text outputs, and pass them to Amazon Lex. Amazon Transcribe can use a custom vocabulary and language model to recognize the terms and concepts related to the reports.
Use Amazon Comprehend to analyze the text inputs and outputs, and extract the relevant information from the reports. Amazon Comprehend can use a custom classifier and entity recognizer to identify the report name, date, metric, or filter from the user's query, and the corresponding data from the reports.
Use a lambda function to implement the business logic and fulfillment of the user's query, such as retrieving the data from the reports, performing calculations or aggregations, and formatting the response. The lambda function can also handle errors and validations, and provide feedback to the user.
Use Amazon Lex to return the response to the user, either in text or speech format, depending on the user's preference.
References:
What Is Amazon Lex?
What Is Amazon Comprehend?
What Is Amazon Transcribe?

NEW QUESTION # 16
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MLS-C01 Latest Exam Materials & MLS-C01 Authentic Exam Questions

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q11-Q16):

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