ExamGecko
Search Search Login
Home Home / Amazon / MLS-C01
Question list
Search
Search

List of questions

Search
Open VPLUS File
Convert VPLUS to PDF

Related questions

A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the Specialist do to integrate the Spark application with SageMaker? (Select THREE)
A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker. Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?
A data engineer needs to provide a team of data scientists with the appropriate dataset to run machine learning training jobs. The data will be stored in Amazon S3. The data engineer is obtaining the data from an Amazon Redshift database and is using join queries to extract a single tabular dataset. A portion of the schema is as follows: ...traction Timestamp (Timeslamp) ...JName(Varchar) ...JNo (Varchar) Th data engineer must provide the data so that any row with a CardNo value of NULL is removed. Also, the TransactionTimestamp column must be separated into a TransactionDate column and a isactionTime column Finally, the CardName column must be renamed to NameOnCard. The data will be extracted on a monthly basis and will be loaded into an S3 bucket. The solution must minimize the effort that is needed to set up infrastructure for the ingestion and transformation. The solution must be automated and must minimize the load on the Amazon Redshift cluster Which solution meets these requirements?
A data scientist receives a collection of insurance claim records. Each record includes a claim ID. the final outcome of the insurance claim, and the date of the final outcome. The final outcome of each claim is a selection from among 200 outcome categories. Some claim records include only partial information. However, incomplete claim records include only 3 or 4 outcome ...gones from among the 200 available outcome categories. The collection includes hundreds of records for each outcome category. The records are from the previous 3 years. The data scientist must create a solution to predict the number of claims that will be in each outcome category every month, several months in advance. Which solution will meet these requirements?
A company is planning a marketing campaign to promote a new product to existing customers. The company has data (or past promotions that are similar. The company decides to try an experiment to send a more expensive marketing package to a smaller number of customers. The company wants to target the marketing campaign to customers who are most likely to buy the new product. The experiment requires that at least 90% of the customers who are likely to purchase the new product receive the marketing materials. ...company trains a model by using the linear learner algorithm in Amazon SageMaker. The model has a recall score of 80% and a precision of 75%. ...should the company retrain the model to meet these requirements?
A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features. Which model will meet the business requirement?
IT leadership wants Jo transition a company's existing machine learning data storage environment to AWS as a temporary ad hoc solution The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learning The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts The solution must also support the storage of csv and JSON files, and be able to query over semi-structured data The following are high priorities for the company: * Solution simplicity * Fast development time * Low cost * High flexibility What technologies meet the company's requirements?
An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?
Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3 The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3 Which approach will provide the information required for further analysis?
A Machine Learning Specialist is using Amazon Sage Maker to host a model for a highly available customer-facing application. The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?

Question 120 - MLS-C01 discussion

Report
Export

IT leadership wants Jo transition a company's existing machine learning data storage environment to AWS as a temporary ad hoc solution The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learning

The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts The solution must also support the storage of csv and JSON files, and be able to query over semi-structured data The following are high priorities for the company:

* Solution simplicity

* Fast development time

* Low cost

* High flexibility

What technologies meet the company's requirements?

A.
Amazon S3 and Amazon Athena
Answers
A.
Amazon S3 and Amazon Athena
B.
Amazon Redshift and AWS Glue
Answers
B.
Amazon Redshift and AWS Glue
C.
Amazon DynamoDB and DynamoDB Accelerator (DAX)
Answers
C.
Amazon DynamoDB and DynamoDB Accelerator (DAX)
D.
Amazon RDS and Amazon ES
Answers
D.
Amazon RDS and Amazon ES
Suggested answer: A

Explanation:

Amazon S3 and Amazon Athena are technologies that meet the company's requirements for a temporary ad hoc solution for machine learning data storage and query. Amazon S3 and Amazon Athena have the following features and benefits:

Amazon S3 is a service that provides scalable, durable, and secure object storage for any type of data. Amazon S3 can store csv and JSON files, as well as other formats, and can handle large volumes of data with high availability and performance. Amazon S3 also integrates with other AWS services, such as Amazon Athena, for further processing and analysis of the data.

Amazon Athena is a service that allows querying data stored in Amazon S3 using standard SQL. Amazon Athena can query over semi-structured data, such as JSON, as well as structured data, such as csv, without requiring any loading or transformation. Amazon Athena is serverless, meaning that there is no infrastructure to manage and users only pay for the queries they run. Amazon Athena also supports the use of AWS Glue Data Catalog, which is a centralized metadata repository that can store and manage the schema and partition information of the data in Amazon S3.

Using Amazon S3 and Amazon Athena, the company can achieve the following high priorities:

Solution simplicity: Amazon S3 and Amazon Athena are easy to use and require minimal configuration and maintenance. The company can simply upload the csv and JSON files to Amazon S3 and use Amazon Athena to query them using SQL. The company does not need to worry about provisioning, scaling, or managing any servers or clusters.

Fast development time: Amazon S3 and Amazon Athena can enable the company to quickly access and analyze the data without any data preparation or loading. The company can use the existing workforce of SQL experts to write and run queries on Amazon Athena and get results in seconds or minutes.

Low cost: Amazon S3 and Amazon Athena are cost-effective and offer pay-as-you-go pricing models. Amazon S3 charges based on the amount of storage used and the number of requests made. Amazon Athena charges based on the amount of data scanned by the queries. The company can also reduce the costs by using compression, encryption, and partitioning techniques to optimize the data storage and query performance.

High flexibility: Amazon S3 and Amazon Athena are flexible and can support various data types, formats, and sources. The company can store and query any type of data in Amazon S3, such as csv, JSON, Parquet, ORC, etc. The company can also query data from multiple sources in Amazon S3, such as data lakes, data warehouses, log files, etc.

The other options are not as suitable as option A for the company's requirements for the following reasons:

Option B: Amazon Redshift and AWS Glue are technologies that can be used for data warehousing and data integration, but they are not ideal for a temporary ad hoc solution. Amazon Redshift is a service that provides a fully managed, petabyte-scale data warehouse that can run complex analytical queries using SQL. AWS Glue is a service that provides a fully managed extract, transform, and load (ETL) service that can prepare and load data for analytics. However, using Amazon Redshift and AWS Glue would require more effort and cost than using Amazon S3 and Amazon Athena. The company would need to load the data from Amazon S3 to Amazon Redshift using AWS Glue, which can take time and incur additional charges. The company would also need to manage the capacity and performance of the Amazon Redshift cluster, which can be complex and expensive.

Option C: Amazon DynamoDB and DynamoDB Accelerator (DAX) are technologies that can be used for fast and scalable NoSQL database and caching, but they are not suitable for the company's data storage and query needs. Amazon DynamoDB is a service that provides a fully managed, key-value and document database that can deliver single-digit millisecond performance at any scale. DynamoDB Accelerator (DAX) is a service that provides a fully managed, in-memory cache for DynamoDB that can improve the read performance by up to 10 times. However, using Amazon DynamoDB and DAX would not allow the company to continue to use SQL as a query language, as Amazon DynamoDB does not support SQL. The company would need to use the DynamoDB API or the AWS SDKs to access and query the data, which can require more coding and learning effort. The company would also need to transform the csv and JSON files into DynamoDB items, which can involve additional processing and complexity.

Option D: Amazon RDS and Amazon ES are technologies that can be used for relational database and search and analytics, but they are not optimal for the company's data storage and query scenario. Amazon RDS is a service that provides a fully managed, relational database that supports various database engines, such as MySQL, PostgreSQL, Oracle, etc. Amazon ES is a service that provides a fully managed, Elasticsearch cluster, which is mainly used for search and analytics purposes. However, using Amazon RDS and Amazon ES would not be as simple and cost-effective as using Amazon S3 and Amazon Athena. The company would need to load the data from Amazon S3 to Amazon RDS, which can take time and incur additional charges. The company would also need to manage the capacity and performance of the Amazon RDS and Amazon ES clusters, which can be complex and expensive. Moreover, Amazon RDS and Amazon ES are not designed to handle semi-structured data, such as JSON, as well as Amazon S3 and Amazon Athena.

References:

Amazon S3

Amazon Athena

Amazon Redshift

AWS Glue

Amazon DynamoDB

[DynamoDB Accelerator (DAX)]

[Amazon RDS]

[Amazon ES]

Show Answer
asked 16/09/2024
Tyler Evans
38 questions
PreviousPreviousNextNext
User
Your answer:
0 comments
Sorted by

Leave a comment first

ExamGecko

Copyright @2023 ExamGecko | All right reserved

Mail us: [email protected]
About us
Contact us
Twitter X
Facebook
Medium
Convert VPLUS to PDF
Open VPLUS files Easily and Quickly
Privacy Policy
Disclaimer
Terms