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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Certification Exam is a highly sought-after certification that recognizes a person's skills and knowledge in the field of machine learning on the Amazon Web Services (AWS) platform. AWS-Certified-Machine-Learning-Specialty Exam is intended for IT professionals who want to demonstrate their expertise in designing, deploying and managing machine learning solutions on AWS.

The MLS-C01 exam covers a wide range of topics related to machine learning, including data preparation, feature engineering, model selection and evaluation, deep learning, and natural language processing (NLP). It also covers AWS-specific topics such as AWS SageMaker, AWS Deep Learning AMIs, AWS Machine Learning APIs, and AWS Machine Learning Services.

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The AWS Certified Machine Learning - Specialty exam consists of 65 multiple-choice and multiple-response questions, and candidates have three hours to complete the exam. AWS-Certified-Machine-Learning-Specialty Exam Tests the candidate's ability to design, implement, deploy, and maintain machine learning solutions on the AWS platform. To be eligible for the exam, candidates need to have a minimum of one year of experience in developing and maintaining machine learning solutions on the AWS platform. Upon successful completion of the exam, candidates will receive the AWS Certified Machine Learning - Specialty certification, which is recognized globally and demonstrates their expertise in the field of machine learning on the AWS platform.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q94-Q99):

NEW QUESTION # 94
A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined The model needs lo be retrained daily Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

A. Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3
B. Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.
C. Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehouse stream that transforms raw record attributes into simple transformed values using SQL.
D. Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3 then use AWS Glue to do the transformation

Answer: C

Explanation:
Amazon Kinesis Data Analytics is a service that can analyze streaming data in real time using SQL or Apache Flink applications. It can also use machine learning algorithms, such as Random Cut Forest (RCF), to perform anomaly detection on streaming data. By inserting a Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream, the retail chain can transform the raw record attributes into simple transformed values using SQL queries. This can be done without changing the existing data ingestion process or deploying additional resources. The transformed records can then be outputted to another Kinesis Data Firehose stream that delivers them to Amazon S3 for training the machine learning model. This approach will require the least amount of development effort, as it leverages the existing Kinesis Data Firehose stream and the built-in SQL capabilities of Kinesis Data Analytics.
References:
* Amazon Kinesis Data Analytics - Amazon Web Services
* Anomaly Detection with Amazon Kinesis Data Analytics - Amazon Web Services
* Amazon Kinesis Data Firehose - Amazon Web Services
* Amazon S3 - Amazon Web Services

NEW QUESTION # 95
Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

A. The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20
85%
B. The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is
1312%
C. The true class frequency for Romance is 0 78 and the predicted class frequency for Adventure is (0 47 -
0.32).
D. The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20 85% ' 0.32

Answer: B

Explanation:
Explanation
The true class frequency for Romance is the percentage of movies that are actually Romance out of all the movies. This can be calculated by dividing the sum of the true values for Romance by the total number of movies. The predicted class frequency for Adventure is the percentage of movies that are predicted to be Adventure out of all the movies. This can be calculated by dividing the sum of the predicted values for Adventure by the total number of movies. Based on the confusion matrix, the true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 13.12%. References: Confusion Matrix, Classification Metrics

NEW QUESTION # 96
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training.
The dataset is stored in Amazon S3 and contains Personally Identifiable Information (Pll). The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?

A. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance.
B. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
C. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
D. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.

Answer: D

NEW QUESTION # 97
A data engineer is preparing a dataset that a retail company will use to predict the number of visitors to stores.
The data engineer created an Amazon S3 bucket. The engineer subscribed the S3 bucket to an AWS Data Exchange data product for general economic indicators. The data engineer wants to join the economic indicator data to an existing table in Amazon Athena to merge with the business data. All these transformations must finish running in 30-60 minutes.
Which solution will meet these requirements MOST cost-effectively?

A. Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda Function Program the Lambda function to run an AWS Glue job that will merge the existing business data with the Athena table Write the results back to Amazon S3.
B. Provision an Amazon Redshift cluster. Subscribe to the AWS Data Exchange product and use the product to create an Amazon Redshift Table Merge the data in Amazon Redshift. Write the results back to Amazon S3.
C. Configure the AWS Data Exchange product as a producer for an Amazon Kinesis data stream. Use an Amazon Kinesis Data Firehose delivery stream to transfer the data to Amazon S3 Run an AWS Glue job that will merge the existing business data with the Athena table. Write the result set back to Amazon S3.
D. Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambda function. Program the Lambda function to use Amazon SageMaker Data Wrangler to merge the existing business data with the Athena table. Write the result set back to Amazon S3.

Answer: D

Explanation:
The most cost-effective solution is to use an S3 event to trigger a Lambda function that uses SageMaker Data Wrangler to merge the data. This solution avoids the need to provision and manage any additional resources, such as Kinesis streams, Firehose delivery streams, Glue jobs, or Redshift clusters. SageMaker Data Wrangler provides a visual interface to import, prepare, transform, and analyze data from various sources, including AWS Data Exchange products. It can also export the data preparation workflow to a Python script that can be executed by a Lambda function. This solution can meet the time requirement of 30-60 minutes, depending on the size and complexity of the data.
Using Amazon S3 Event Notifications
Prepare ML Data with Amazon SageMaker Data Wrangler
AWS Lambda Function

NEW QUESTION # 98
An Machine Learning Specialist discover the following statistics while experimenting on a model.

What can the Specialist from the experiments?

A. The model in Experiment 1 had a high bias error and a high variance error that were reduced in Experiment 3 by regularization Experiment 2 shows thai high bias cannot be reduced by increasing layers and neurons in the model
B. The model In Experiment 1 had a high variance error lhat was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal bias error in Experiment 1
C. The model in Experiment 1 had a high bias error that was reduced in Experiment 3 by regularization Experiment 2 shows that there is minimal variance error in Experiment 1
D. The model in Experiment 1 had a high random noise error that was reduced in Expenment 3 by regularization Expenment 2 shows that random noise cannot be reduced by increasing layers and neurons in the model

Answer: A

NEW QUESTION # 99
......

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AWS-Certified-Machine-Learning-Specialty New Braindumps Book | Reliable AWS-Certified-Machine-Learning-Specialty Dumps

Newest AWS-Certified-Machine-Learning-Specialty New Braindumps Book & Leading Provider in Qualification Exams & Updated Reliable AWS-Certified-Machine-Learning-Specialty Dumps

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q94-Q99):