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Free MLS-C01 Exam Braindumps (page: 33)

Page 33 of 84
PDF with 332 Questions

A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream. As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.

Which next step is MOST likely to improve the data ingestion rate into Amazon S3?

  1. Increase the number of S3 prefixes for the delivery stream to write to.
  2. Decrease the retention period for the data stream.
  3. Increase the number of shards for the data stream.
  4. Add more consumers using the Kinesis Client Library (KCL).

Answer(s): C



A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.


How should the data scientist split the dataset into a training and test set for this use case?

  1. Shuffle all interaction data. Split off the last 10% of the interaction data for the test set.
  2. Identify the most recent 10% of interactions for each user. Split off these interactions for the test set.
  3. Identify the 10% of users with the least interaction data. Split off all interaction data from these users for the test set.
  4. Randomly select 10% of the users. Split off all interaction data from these users for the test set.

Answer(s): D



A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial data. The company is worried about data egress and wants an ML engineer to secure the environment.

Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

  1. Connect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
  2. Use SCPs to restrict access to SageMaker.
  3. Disable root access on the SageMaker notebook instances.
  4. Enable network isolation for training jobs and models.
  5. Restrict notebook presigned URLs to specific IPs used by the company.
  6. Protect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys.

Answer(s): A,D,E



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 data. 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?


  1. -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

  2. -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

  3. -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

  4. -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
    -Amazon QuickSight for reporting and getting insights

Answer(s): A



Page 33 of 84



Post your Comments and Discuss Amazon MLS-C01 exam with other Community members:

Richard commented on October 24, 2023
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Anonymous
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Richard commented on October 24, 2023
I am thrilled to say that I passed my Amazon Web Services MLS-C01 exam, thanks to study materials. They were comprehensive and well-structured, making my preparation efficient.
Anonymous
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Ken commented on October 13, 2021
I would like to share my good news with you about successfully passing my exam. This study package is very relevant and helpful.
AUSTRALIA
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Alex commented on April 19, 2021
A very great in amount of questions are from real exam. Almost same wording. :)
SOUTH KOREA
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MD ABU S CHOWDHURY commented on January 18, 2020
Working on the test..
UNITED STATES
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