PROVIDING YOU PROFESSIONAL MLS-C01 LATEST TEST MATERIALS WITH 100% PASSING GUARANTEE

Providing You Professional MLS-C01 Latest Test Materials with 100% Passing Guarantee

Providing You Professional MLS-C01 Latest Test Materials with 100% Passing Guarantee

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q216-Q221):

NEW QUESTION # 216
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

  • A. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
  • B. Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
  • C. Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
  • D. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.

Answer: A


NEW QUESTION # 217
A network security vendor needs to ingest telemetry data from thousands of endpoints that run all over the world. The data is transmitted every 30 seconds in the form of records that contain 50 fields. Each record is up to 1 KB in size. The security vendor uses Amazon Kinesis Data Streams to ingest the data. The vendor requires hourly summaries of the records that Kinesis Data Streams ingests. The vendor will use Amazon Athena to query the records and to generate the summaries. The Athena queries will target 7 to 12 of the available data fields.
Which solution will meet these requirements with the LEAST amount of customization to transform and store the ingested data?

  • A. Use Amazon Kinesis Data Analytics to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.
  • B. Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using a short-lived Amazon EMR cluster.
  • C. Use Amazon Kinesis Data Firehose to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using AWS Lambda.
  • D. Use AWS Lambda to read and aggregate the data hourly. Transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose.

Answer: A

Explanation:
The solution that will meet the requirements with the least amount of customization to transform and store the ingested data is to use Amazon Kinesis Data Analytics to read and aggregate the data hourly, transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose. This solution leverages the built-in features of Kinesis Data Analytics to perform SQL queries on streaming data and generate hourly summaries.
Kinesis Data Analytics can also output the transformed data to Kinesis Data Firehose, which can then deliver the data to S3 in a specified format and partitioning scheme. This solution does not require any custom code or additional infrastructure to process the data. The other solutions either require more customization (such as using Lambda or EMR) or do not meet the requirement of aggregating the data hourly (such as using Lambda to read the data from Kinesis Data Streams). References:
* 1: Boosting Resiliency with an ML-based Telemetry Analytics Architecture | AWS Architecture Blog
* 2: AWS Cloud Data Ingestion Patterns and Practices
* 3: IoT ingestion and Machine Learning analytics pipeline with AWS IoT ...
* 4: AWS IoT Data Ingestion Simplified 101: The Complete Guide - Hevo Data


NEW QUESTION # 218
A large company has developed a B1 application that generates reports and dashboards using data collected from various operational metrics The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports The company wants the executives to be able ask questions using written and spoken interlaces Which combination of services can be used to build this conversational interface? (Select THREE )

  • A. Amazon Poly
  • B. Amazon Connect
  • C. Amazon Lex
  • D. Alexa for Business
  • E. Amazon Comprehend
  • F. Amazon Transcribe

Answer: B,E,F


NEW QUESTION # 219
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 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
  • B. 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
  • C. 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
  • D. 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

Answer: D

Explanation:
Amazon QuickSight for reporting and getting insights


NEW QUESTION # 220
A machine learning (ML) specialist uploads a dataset to an Amazon S3 bucket that is protected by server-side encryption with AWS KMS keys (SSE-KMS). The ML specialist needs to ensure that an Amazon SageMaker notebook instance can read the dataset that is in Amazon S3.
Which solution will meet these requirements?

  • A. Define security groups to allow all HTTP inbound and outbound traffic. Assign the security groups to the SageMaker notebook instance.
  • B. Assign an IAM role that provides S3 read access for the dataset to the SageMaker notebook. Grant permission in the KMS key policy to the 1AM role.
  • C. Assign the same KMS key that encrypts the data in Amazon S3 to the SageMaker notebook instance.
  • D. Configure the SageMaker notebook instance to have access to the VPC. Grant permission in the AWS Key Management Service (AWS KMS) key policy to the notebook's VPC.

Answer: B

Explanation:
When an Amazon SageMaker notebook instance needs to access encrypted data in Amazon S3, the ML specialist must ensure that both Amazon S3 access permissions and AWS Key Management Service (KMS) decryption permissions are properly configured. The dataset in this scenario is stored with server-side encryption using an AWS KMS key (SSE-KMS), so the following steps are necessary:
* S3 Read Permissions: Attach an IAM role to the SageMaker notebook instance with permissions that allow the s3:GetObject action for the specific S3 bucket storing the data. This will allow the notebook instance to read data from Amazon S3.
* KMS Key Policy Permissions: Grant permissions in the KMS key policy to the IAM role assigned to the SageMaker notebook instance. This allows SageMaker to use the KMS key to decrypt data in the S3 bucket.
These steps ensure the SageMaker notebook instance can access the encrypted data stored in S3. The AWS documentation emphasizes that to access SSE-KMS encrypted data, the SageMaker notebook requires appropriate permissions in both the S3 bucket policy and the KMS key policy, making Option C the correct and secure approach.


NEW QUESTION # 221
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