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Read the AWS Certified Machine Learning Specialty Exam formate below

Cost: 300$
Format: Multiple choices, multiple answers
Language: English
Length of Examination: 170 minutes
Passing score: 750

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) certification exam is designed to test the knowledge and expertise of professionals in the field of machine learning. MLS-C01 exam is intended for individuals who have a solid understanding of machine learning concepts and practices and are looking to validate their skills and knowledge in this area. MLS-C01 Exam is designed to test the candidate's ability to design, implement, and maintain machine learning solutions on the AWS platform.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) certification exam is designed for individuals who want to validate their expertise in machine learning (ML) on the Amazon Web Services (AWS) platform. AWS Certified Machine Learning - Specialty certification is ideal for data scientists, machine learning developers, and individuals who want to demonstrate their skills in building, training, deploying, and managing ML models on AWS.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q183-Q188):

NEW QUESTION # 183
A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users.
The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company's business goals. To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models.
Which solution satisfies these requirements with MINIMAL effort?

A. Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.
B. Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices. Programmatically control which model is invoked for inference based on the medical device type.
C. Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.
D. Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer.

Answer: A

Explanation:
Explanation
Amazon SageMaker is a service that allows users to build, train, and deploy ML models on AWS. Amazon SageMaker endpoints are scalable and secure web services that can be used to perform real-time inference on ML models. An endpoint configuration defines the models that are deployed and the resources that are used by the endpoint. An endpoint configuration can have multiple production variants, each representing a different version or variant of a model. Users can specify the portion of the inferences served by each production variant using the initialVariantWeight parameter. Users can also programmatically update the endpoint configuration to change the portion of the inferences served by each production variant using the UpdateEndpointWeightsAndCapacities API. Therefore, option B is the best solution to satisfy the requirements with minimal effort.
Option A is incorrect because creating multiple endpoints for each model would incur more cost and complexity than using a single endpoint with multiple production variants. Moreover, controlling the invocation of different models at the application layer would require more custom logic and coordination than using the UpdateEndpointWeightsAndCapacities API. Option C is incorrect because Amazon SageMaker Neo is a service that allows users to optimize ML models for different hardware platforms, such as edge devices. It is not relevant to the problem of running multiple versions of a model in parallel for long periods of time.
Option D is incorrect because Amazon SageMaker batch transform is a service that allows users to perform asynchronous inference on large datasets. It is not suitable for the problem of performing real-time inference on streaming data from device users.
References:
Deploying models to Amazon SageMaker hosting services - Amazon SageMaker Update an Amazon SageMaker endpoint to accommodate new models - Amazon SageMaker UpdateEndpointWeightsAndCapacities - Amazon SageMaker

NEW QUESTION # 184
A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s) Which visualization will accomplish this?

A. A scatter plot showing (he performance of the objective metric over each training iteration
B. A scatter plot with points colored by target variable that uses (-Distributed Stochastic Neighbor Embedding (I-SNE) to visualize the large number of input variables in an easier-to-read dimension.
C. A histogram showing whether the most important input feature is Gaussian.
D. A scatter plot showing the correlation between maximum tree depth and the objective metric.

Answer: B

NEW QUESTION # 185
A company wants to enhance audits for its machine learning (ML) systems. The auditing system must be able to perform metadata analysis on the features that the ML models use. The audit solution must generate a report that analyzes the metadata. The solution also must be able to set the data sensitivity and authorship of features.
Which solution will meet these requirements with the LEAST development effort?

A. Use Amazon SageMaker Features Store to apply custom algorithms to analyze the feature-level metadata that the company requires. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.
B. Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use Amazon QuickSight to analyze the metadata.
C. Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use SageMaker Studio to analyze the metadata.
D. Use Amazon SageMaker Feature Store to select the features. Create a data flow to perform feature-level metadata analysis. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.

Answer: B

Explanation:
The solution that will meet the requirements with the least development effort is to use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use, assign the required metadata for each feature, and use Amazon QuickSight to analyze the metadata. This solution can leverage the existing AWS services and features to perform feature-level metadata analysis and reporting.
Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share machine learning (ML) features. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference workflows. A feature group is a logical grouping of ML features whose organization and structure is defined by a feature group schema. A feature group schema consists of a list of feature definitions, each of which specifies the name, type, and metadata of a feature. The metadata can include information such as data sensitivity, authorship, description, and parameters. The metadata can help make features discoverable, understandable, and traceable. Amazon SageMaker Feature Store allows users to set feature groups for the current features that the ML models use, and assign the required metadata for each feature using the AWS SDK for Python (Boto3), AWS Command Line Interface (AWS CLI), or Amazon SageMaker Studio1.
Amazon QuickSight is a fully managed, serverless business intelligence service that makes it easy to create and publish interactive dashboards that include ML insights. Amazon QuickSight can connect to various data sources, such as Amazon S3, Amazon Athena, Amazon Redshift, and Amazon SageMaker Feature Store, and analyze the data using standard SQL or built-in ML-powered analytics. Amazon QuickSight can also create rich visualizations and reports that can be accessed from any device, and securely shared with anyone inside or outside an organization. Amazon QuickSight can be used to analyze the metadata of the features stored in Amazon SageMaker Feature Store, and generate a report that summarizes the metadata analysis2.
The other options are either more complex or less effective than the proposed solution. Using Amazon SageMaker Data Wrangler to select the features and create a data flow to perform feature-level metadata analysis would require additional steps and resources, and may not capture all the metadata attributes that the company requires. Creating an Amazon DynamoDB table to store feature-level metadata would introduce redundancy and inconsistency, as the metadata is already stored in Amazon SageMaker Feature Store. Using SageMaker Studio to analyze the metadata would not generate a report that can be easily shared and accessed by the company.
1: Amazon SageMaker Feature Store - Amazon Web Services
2: Amazon QuickSight - Business Intelligence Service - Amazon Web Services

NEW QUESTION # 186
A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below.
Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values.
What technique should be used to convert this column to binary values.

A. Binarization
B. One-hot encoding
C. Normalization transformation
D. Tokenization

Answer: B

Explanation:
One-hot encoding is a technique that can be used to convert a categorical variable, such as the Day-Of_Week column, to binary values. One-hot encoding creates a new binary column for each unique value in the original column, and assigns a value of 1 to the column that corresponds to the value in the original column, and 0 to the rest. For example, if the original column has values Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday, one-hot encoding will create seven new columns, each representing one day of the week. If the value in the original column is Tuesday, then the column for Tuesday will have a value of 1, and the other columns will have a value of 0. One-hot encoding can help improve the performance of machine learning models, as it eliminates the ordinal relationship between the values and creates a more informative and sparse representation of the data.
One-Hot Encoding - Amazon SageMaker
One-Hot Encoding: A Simple Guide for Beginners | by Jana Schmidt ...
One-Hot Encoding in Machine Learning | by Nishant Malik | Towards ...

NEW QUESTION # 187
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 clustering on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.
C. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
D. Use regression on customer profile data to understand key characteristics of consumer segments Find similar profiles on social media.

Answer: B

Explanation:
Explanation
Clustering is a machine learning technique that can group data points into clusters based on their similarity or proximity. Clustering can help discover the underlying structure and patterns in the data, as well as identify outliers or anomalies. Clustering can also be used for customer segmentation, which is the process of dividing customers into groups based on their characteristics, behaviors, preferences, or needs. Customer segmentation can help understand the key features and needs of different customer segments, as well as design and implement targeted marketing campaigns for each segment. In this case, the Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers.
To do this, the Manager can use clustering on customer profile data to understand the key characteristics of consumer segments, such as their demographics, pet types, policy preferences, premiums paid, claims made, etc. The Manager can then find similar profiles on social media, such as Facebook, Twitter, Instagram, etc., by using the cluster features as filters or keywords. The Manager can then target these potential new customers with personalized and relevant ads or offers that match their segment's needs and interests. This way, the Manager can implement a machine learning model to identify potential new customers on social media.

NEW QUESTION # 188
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