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The above formats of SurePassExams are made to help customers prepare as per their unique styles and crack the AWS-Certified-Machine-Learning-Specialty exam certification on the very first attempt. Our AWS Certified Machine Learning - Specialty (AWS-Certified-Machine-Learning-Specialty) questions product is getting updated regularly as per the original AWS Certified Machine Learning - Specialty (AWS-Certified-Machine-Learning-Specialty) practice test's content. So that customers can prepare according to the latest AWS-Certified-Machine-Learning-Specialty exam content and pass it with ease.

To earn the AWS Certified Machine Learning - Specialty certification, candidates must pass the MLS-C01 exam, which consists of 65 multiple-choice and multiple-response questions. AWS-Certified-Machine-Learning-Specialty exam covers a broad range of topics, including data engineering, feature engineering, model selection and training, and deployment and monitoring of ML models. AWS-Certified-Machine-Learning-Specialty Exam also tests the candidate's knowledge of AWS's various ML services, such as Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q283-Q288):

NEW QUESTION # 283
A data scientist is working on a forecast problem by using a dataset that consists of .csv files that are stored in Amazon S3. The files contain a timestamp variable in the following format:
March 1st, 2020, 08:14pm -
There is a hypothesis about seasonal differences in the dependent variable. This number could be higher or lower for weekdays because some days and hours present varying values, so the day of the week, month, or hour could be an important factor. As a result, the data scientist needs to transform the timestamp into weekdays, month, and day as three separate variables to conduct an analysis.
Which solution requires the LEAST operational overhead to create a new dataset with the added features?

A. Create an Amazon EMR cluster. Develop PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
B. Create an AWS Glue job. Develop code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
C. Create a processing job in Amazon SageMaker. Develop Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
D. Create a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize date/time transform to generate the new variables, and save the dataset as a new file in Amazon S3.

Answer: D

Explanation:
Explanation
The solution C will create a new dataset with the added features with the least operational overhead because it uses Amazon SageMaker Data Wrangler, which is a service that simplifies the process of data preparation and feature engineering for machine learning. The solution C involves the following steps:
Create a new flow in Amazon SageMaker Data Wrangler. A flow is a visual representation of the data preparation steps that can be applied to one or more datasets. The data scientist can create a new flow in the Amazon SageMaker Studio interface and import the S3 file as a data source1.
Use the Featurize date/time transform to generate the new variables. Amazon SageMaker Data Wrangler provides a set of preconfigured transformations that can be applied to the data with a few clicks. The Featurize date/time transform can parse a date/time column and generate new columns for the year, month, day, hour, minute, second, day of week, and day of year. The data scientist can use this transform to create the new variables from the timestamp variable2.
Save the dataset as a new file in Amazon S3. Amazon SageMaker Data Wrangler can export the transformed dataset as a new file in Amazon S3, or as a feature store in Amazon SageMaker Feature Store. The data scientist can choose the output format and location of the new file3.
The other options are not suitable because:
Option A: Creating an Amazon EMR cluster and developing PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3 will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to manage the Amazon EMR cluster, the PySpark application, and the data storage. Moreover, the data scientist will have to write custom code for the date/time parsing and feature generation, which may require more development effort and testing4.
Option B: Creating a processing job in Amazon SageMaker and developing Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3 will incur more operational overhead than using Amazon SageMaker Data Wrangler.
The data scientist will have to manage the processing job, the Python code, and the data storage. Moreover, the data scientist will have to write custom code for the date/time parsing and feature generation, which may require more development effort and testing5.
Option D: Creating an AWS Glue job and developing code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3 will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to manage the AWS Glue job, the code, and the data storage. Moreover, the data scientist will have to write custom code for the date/time parsing and feature generation, which may require more development effort and testing6.
References:
1: Amazon SageMaker Data Wrangler
2: Featurize Date/Time - Amazon SageMaker Data Wrangler
3: Exporting Data - Amazon SageMaker Data Wrangler
4: Amazon EMR
5: Processing Jobs - Amazon SageMaker
6: AWS Glue

NEW QUESTION # 284
A company is using Amazon SageMaker to build a machine learning (ML) model to predict customer churn based on customer call transcripts. Audio files from customer calls are located in an on-premises VoIP system that has petabytes of recorded calls. The on-premises infrastructure has high-velocity networking and connects to the company's AWS infrastructure through a VPN connection over a 100 Mbps connection.
The company has an algorithm for transcribing customer calls that requires GPUs for inference. The company wants to store these transcriptions in an Amazon S3 bucket in the AWS Cloud for model development.
Which solution should an ML specialist use to deliver the transcriptions to the S3 bucket as quickly as possible?

A. Order and use AWS Outposts to run the transcription algorithm on GPU-based Amazon EC2 instances.
Store the resulting transcriptions in the transcription S3 bucket.
B. Order and use an AWS Snowcone device with Amazon EC2 Inf1 instances to run the transcription algorithm Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket
C. Use AWS DataSync to ingest the audio files to Amazon S3. Create an AWS Lambda function to run the transcription algorithm on the audio files when they are uploaded to Amazon S3. Configure the function to write the resulting transcriptions to the transcription S3 bucket.
D. Order and use an AWS Snowball Edge Compute Optimized device with an NVIDIA Tesla module to run the transcription algorithm. Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket.

Answer: D

NEW QUESTION # 285
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 introduce non-linear dependencies within the data which could invalidate the linear assumptions of the model
B. It could modify the loss function during optimization causing it to fail during training
C. It could cause the backpropagation algorithm to fail during training
D. It could create a singular matrix during optimization which fails to define a unique solution

Answer: B

NEW QUESTION # 286
A Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?

A. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
B. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.
C. AWS Glue with a custom ETL script to transform the data.
D. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.

Answer: A

Explanation:
* Amazon Kinesis Data Analytics is a service that enables you to analyze streaming data in real time using SQL or Apache Flink applications. You can use Kinesis Data Analytics to process and gain insights from data streams such as web logs, clickstreams, IoT data, and more.
* To use SQL to query a data stream of GZIP files, you need to first transform the data into a format that Kinesis Data Analytics can understand, such as JSON, CSV, or Apache Parquet. You can use an AWS Lambda function to perform this transformation and send the output to a Kinesis data stream that is connected to your Kinesis Data Analytics application. This way, you can use SQL to query the stream with the least latency, as Lambda functions are triggered in near real time by the incoming data and Kinesis Data Analytics can process the data as soon as it arrives.
* The other options are not optimal for this scenario, as they introduce more latency or complexity. AWS Glue is a serverless data integration service that can perform ETL (extract, transform, and load) tasks on data sources, but it is not designed for real-time streaming data analysis. An Amazon Kinesis Client Library is a Java library that enables you to build custom applications that process data from Kinesis data streams, but it requires more coding and configuration than using a Lambda function. Amazon Kinesis Data Firehose is a service that can deliver streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk, but it does not support SQL queries on the data.
What Is Amazon Kinesis Data Analytics for SQL Applications?
Using AWS Lambda with Amazon Kinesis Data Streams
Using AWS Lambda with Amazon Kinesis Data Firehose

NEW QUESTION # 287
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 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.
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 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.
D. 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.

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.
References:
1: Amazon SageMaker Feature Store - Amazon Web Services
2: Amazon QuickSight - Business Intelligence Service - Amazon Web Services

NEW QUESTION # 288
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RELIABLE ONLINE AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY TRAINING & LEADING PROVIDER IN QUALIFICATION EXAMS & VERIFIED AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY VALID TEST ANSWERS

Reliable Online AWS-Certified-Machine-Learning-Specialty Training & Leading Provider in Qualification Exams & Verified AWS-Certified-Machine-Learning-Specialty Valid Test Answers