AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY TORRENT | 100% FREE LATEST AWS CERTIFIED MACHINE LEARNING - SPECIALTY DUMPS

AWS-Certified-Machine-Learning-Specialty Torrent | 100% Free Latest AWS Certified Machine Learning - Specialty Dumps

AWS-Certified-Machine-Learning-Specialty Torrent | 100% Free Latest AWS Certified Machine Learning - Specialty Dumps

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Understanding functional and technical aspects of AWS Certified Machine Learning - Specialty Modeling

The following will be discussed in AMAZON MLS-C01 exam dumps:

  • Frame business problems as machine learning problems
  • Select the appropriate model(s) for a given machine learning problem
  • Evaluate machine learning models
  • Train machine learning models
  • Perform hyperparameter optimization

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q169-Q174):

NEW QUESTION # 169
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 a zero mean.
  • B. Linear regression is appropriate. The residuals have constant variance.
  • C. Linear regression is inappropriate. The underlying data has outliers.
  • D. Linear regression is inappropriate. The residuals do not have constant variance.

Answer: D

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 # 170
A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may be fraudulent How should the Specialist frame this business problem'?

  • A. Multi-category classification
  • B. Streaming classification
  • C. Regression classification
  • D. Binary classification

Answer: B


NEW QUESTION # 171
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 regression 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 a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media

Answer: B

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 # 172
A Machine Learning Specialist deployed a model that provides product recommendations on a company's website. Initially, the model was performing very well and resulted in customers buying more products on average. However, within the past few months, the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less.
The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago.
Which method should the Specialist try to improve model performance?

  • A. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes
  • B. The model should be periodically retrained using the original training data plus new data as product inventory changes.
  • C. The model's hyperparameters should be periodically updated to prevent drift.
  • D. The model needs to be completely re-engineered because it is unable to handle product inventory changes.

Answer: B


NEW QUESTION # 173
A company is building a new version of a recommendation engine. Machine learning (ML) specialists need to keep adding new data from users to improve personalized recommendations. The ML specialists gather data from the users' interactions on the platform and from sources such as external websites and social media.
The pipeline cleans, transforms, enriches, and compresses terabytes of data daily, and this data is stored in Amazon S3. A set of Python scripts was coded to do the job and is stored in a large Amazon EC2 instance.
The whole process takes more than 20 hours to finish, with each script taking at least an hour. The company wants to move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers.
Which approach will address all of these requirements with the LEAST development effort?

  • A. Create an AWS Glue job. Convert the scripts to PySpark. Execute the pipeline. Store the results in Amazon S3.
  • B. Load the data into Amazon DynamoDB. Convert the scripts to an AWS Lambda function. Execute the pipeline by triggering Lambda executions. Store the results in Amazon S3.
  • C. Create a set of individual AWS Lambda functions to execute each of the scripts. Build a step function by using the AWS Step Functions Data Science SDK. Store the results in Amazon S3.
  • D. Load the data into an Amazon Redshift cluster. Execute the pipeline by using SQL. Store the results in Amazon S3.

Answer: A

Explanation:
The best approach to address all of the requirements with the least development effort is to create an AWS Glue job, convert the scripts to PySpark, execute the pipeline, and store the results in Amazon S3. This is because:
* AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analytics 1. AWS Glue can run Python and Scala scripts to process data from various sources, such as Amazon S3, Amazon DynamoDB, Amazon Redshift, and more 2. AWS Glue also provides a serverless Apache Spark environment to run ETL jobs, eliminating the need to provision and manage servers 3.
* PySpark is the Python API for Apache Spark, a unified analytics engine for large-scale data processing 4. PySpark can perform various data transformations and manipulations on structured and unstructured data, such as cleaning, enriching, and compressing 5. PySpark can also leverage the distributed computing power of Spark to handle terabytes of data efficiently and scalably 6.
* By creating an AWS Glue job and converting the scripts to PySpark, the company can move the scripts out of Amazon EC2 into a more managed solution that will eliminate the need to maintain servers. The company can also reduce the development effort by using the AWS Glue console, AWS SDK, or AWS CLI to create and run the job 7. Moreover, the company can use the AWS Glue Data Catalog to store and manage the metadata of the data sources and targets 8.
The other options are not as suitable as option C for the following reasons:
* Option A is not optimal because loading the data into an Amazon Redshift cluster and executing the pipeline by using SQL will incur additional costs and complexity for the company. Amazon Redshift is a fully managed data warehouse service that enables fast and scalable analysis of structured data .
However, it is not designed for ETL purposes, such as cleaning, transforming, enriching, and compressing data. Moreover, using SQL to perform these tasks may not be as expressive and flexible as using Python scripts. Furthermore, the company will have to provision and configure the Amazon Redshift cluster, and load and unload the data from Amazon S3, which will increase the development effort and time.
* Option B is not feasible because loading the data into Amazon DynamoDB and converting the scripts to an AWS Lambda function will not work for the company's use case. Amazon DynamoDB is a fully managed key-value and document database service that provides fast and consistent performance at any scale . However, it is not suitable for storing and processing terabytes of data daily, as it has limits on the size and throughput of each table and item . Moreover, using AWS Lambda to execute the pipeline will not be efficient or cost-effective, as Lambda has limits on the memory, CPU, and execution time of each function . Therefore, using Amazon DynamoDB and AWS Lambda will not meet the company's requirements for processing large amounts of data quickly and reliably.
* Option D is not relevant because creating a set of individual AWS Lambda functions to execute each of the scripts and building a step function by using the AWS Step Functions Data Science SDK will not address the main issue of moving the scripts out of Amazon EC2. AWS Step Functions is a fully managed service that lets you coordinate multiple AWS services into serverless workflows . The AWS Step Functions Data Science SDK is an open source library that allows data scientists to easily create workflows that process and publish machine learning models using Amazon SageMaker and AWS Step Functions . However, these services and tools are not designed for ETL purposes, such as cleaning, transforming, enriching, and compressing data. Moreover, as mentioned in option B, using AWS Lambda to execute the scripts will not be efficient or cost-effective for the company's use case.
References:
* What Is AWS Glue?
* AWS Glue Components
* AWS Glue Serverless Spark ETL
* PySpark - Overview
* PySpark - RDD
* PySpark - SparkContext
* Adding Jobs in AWS Glue
* Populating the AWS Glue Data Catalog
* [What Is Amazon Redshift?]
* [What Is Amazon DynamoDB?]
* [Service, Account, and Table Quotas in DynamoDB]
* [AWS Lambda quotas]
* [What Is AWS Step Functions?]
* [AWS Step Functions Data Science SDK for Python]


NEW QUESTION # 174
......

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