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With the AWS Certified Machine Learning - Specialty MLS-C01 exam, you will have the chance to update your knowledge while obtaining dependable evidence of your proficiency. You can benefit from a number of additional benefits after completing the AWS Certified Machine Learning - Specialty MLS-C01 Certification Exam. But keep in mind that the MLS-C01 certification test is a worthwhile and challenging certificate.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q268-Q273):

NEW QUESTION # 268
A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.
Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

A. Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.
B. Concatenate the features with high correlation scores by using a Jupyter notebook.
C. Drop the features with low correlation scores by using a Jupyter notebook.
D. Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.

Answer: D

NEW QUESTION # 269
A Machine Learning Specialist needs to move and transform data in preparation for training Some of the data needs to be processed in near-real time and other data can be moved hourly There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data Which of the following services can feed data to the MapReduce jobs? (Select TWO )

A. Amazon ES
B. Amazon Athena
C. AWSDMS
D. Amazon Kinesis
E. AWS Data Pipeline

Answer: D,E

Explanation:
Explanation
Amazon Kinesis and AWS Data Pipeline are two services that can feed data to the Amazon EMR MapReduce jobs. Amazon Kinesis is a service that can ingest, process, and analyze streaming data in real time. Amazon Kinesis can be integrated with Amazon EMR to run MapReduce jobs on streaming data sources, such as web logs, social media, IoT devices, and clickstreams. Amazon Kinesis can handle data that needs to be processed in near-real time, such as for anomaly detection, fraud detection, or dashboarding. AWS Data Pipeline is a service that can orchestrate and automate data movement and transformation across various AWS services and on-premises data sources. AWS Data Pipeline can be integrated with Amazon EMR to run MapReduce jobs on batch data sources, such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon Redshift. AWS Data Pipeline can handle data that can be moved hourly, such as for data warehousing, reporting, or machine learning.
AWSDMS is not a valid service name. AWS Database Migration Service (AWS DMS) is a service that can migrate data from various sources to various targets, but it does not support streaming data or MapReduce jobs.
Amazon Athena is a service that can query data stored in Amazon S3 using standard SQL, but it does not feed data to Amazon EMR or run MapReduce jobs.
Amazon ES is a service that provides a fully managed Elasticsearch cluster, which can be used for search, analytics, and visualization, but it does not feed data to Amazon EMR or run MapReduce jobs. References:
Using Amazon Kinesis with Amazon EMR - Amazon EMR
AWS Data Pipeline - Amazon Web Services
Using AWS Data Pipeline to Run Amazon EMR Jobs - AWS Data Pipeline

NEW QUESTION # 270
A company needs to quickly make sense of a large amount of data and gain insight from it. The data is in different formats, the schemas change frequently, and new data sources are added regularly. The company wants to use AWS services to explore multiple data sources, suggest schemas, and enrich and transform the dat a. The solution should require the least possible coding effort for the data flows and the least possible infrastructure management.
Which combination of AWS services will meet these requirements?

A. AWS Glue for data discovery, enrichment, and transformation
Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
B. Amazon EMR for data discovery, enrichment, and transformation
Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL Amazon QuickSight for reporting and getting insights
C. AWS Data Pipeline for data transfer
AWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformation Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL
D. Amazon Kinesis Data Analytics for data ingestion
Amazon EMR for data discovery, enrichment, and transformation
Amazon Redshift for querying and analyzing the results in Amazon S3

Answer: B

Explanation:
Amazon QuickSight for reporting and getting insights

NEW QUESTION # 271
A Machine Learning Specialist is applying a linear least squares regression model to a dataset with 1 000 records and 50 features Prior to training, the ML Specialist notices that two features are perfectly linearly dependent Why could this be an issue for the linear least squares regression model?

A. It could cause the backpropagation algorithm to fail during training
B. It could modify the loss function during optimization causing it to fail during training
C. It could create a singular matrix during optimization which fails to define a unique solution
D. It could introduce non-linear dependencies within the data which could invalidate the linear assumptions of the model

Answer: C

Explanation:
Linear least squares regression is a method of fitting a linear model to a set of data by minimizing the sum of squared errors between the observed and predicted values. The solution of the linear least squares problem can be obtained by solving the normal equations, which are given by ATAx=ATb, where A is the matrix of explanatory variables, b is the vector of response variables, and x is the vector of unknown coefficients.
However, if the matrix A has two features that are perfectly linearly dependent, then the matrix ATA will be singular, meaning that it does not have a unique inverse. This implies that the normal equations do not have a unique solution, and the linear least squares problem is ill-posed. In other words, there are infinitely many values of x that can satisfy the normal equations, and the linear model is not identifiable.
This can be an issue for the linear least squares regression model, as it can lead to instability, inconsistency, and poor generalization of the model. It can also cause numerical difficulties when trying to solve the normal equations using computational methods, such as matrix inversion or decomposition. Therefore, it is advisable to avoid or remove the linearly dependent features from the matrix A before applying the linear least squares regression model.
References:
Linear least squares (mathematics)
Linear Regression in Matrix Form
Singular Matrix Problem

NEW QUESTION # 272
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?

A. Normal distribution
B. Uniform distribution
C. Poisson distribution
D. Binomial distribution

Answer: D

NEW QUESTION # 273
......

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