MLS-C01 Exam: Absolutely Free Online Practice

Question 1
- A company is building a pipeline that periodically retrains its machine learning (ML) models by using new streaming data from devices. The company's data engineering team wants to build a data ingestion system that has high throughput, durable storage, and scalability. The company can tolerate up to 5 minutes of latency for data ingestion. The company needs a solution that can apply basic data transformation during the ingestion process. Which solution will meet these requirements with the MOST operational efficiency?
  A : Configure the devices to send streaming data to an Amazon Kinesis data stream. Configure an Amazon Kinesis Data Firehose delivery stream to automatically consume the Kinesis data stream, transform the data with an AWS Lambda function, and save the output into an Amazon S3 bucket
  
  B : Configure the devices to send streaming data to an Amazon S3 bucket. Configure an AWS Lambda function that is invoked by S3 event notifications to transform the data and load the data into an Amazon Kinesis data stream. Configure an Amazon Kinesis Data Firehose delivery stream to automatically consume the Kinesis data stream and load the output back into the S3 bucket.
  
  C : Configure the devices to send streaming data to an Amazon S3 bucket. Configure an AWS Glue job that is invoked by S3 event notifications to read the data, transform the data, and load the output into a new S3 bucket.
  
  D : Configure the devices to send streaming data to an Amazon Kinesis Data Firehose delivery stream. Configure an AWS Glue job that connects to the delivery stream to transform the data and load the output into an Amazon S3 bucket.
  
  Answer: A
Question 2
- A data scientist is designing a repository that will contain many images of vehicles. The repository must scale automatically in size to store new images every day. The repository must support versioning of the images. The data scientist must implement a solution that maintains multiple immediately accessible copies of the data in different AWS Regions.
  Which solution will meet these requirements?
  A : Amazon S3 with S3 Cross-Region Replication (CRR)
  
  B : Amazon Elastic Block Store (Amazon EBS) with snapshots that are shared in a secondary Region
  
  C : Amazon Elastic File System (Amazon EFS) Standard storage that is configured with Regional availability
  
  D : AWS Storage Gateway Volume Gateway
  
  Answer: A
Question 3
- A machine learning specialist needs to analyze comments on a news website with users across the globe. The specialist must find the most discussed topics in the comments that are in either English or Spanish. What steps could be used to accomplish this task? (Choose two.)
  
  A : Use an Amazon SageMaker BlazingText algorithm to find the topics independently from language. Proceed with the analysis.
  
  B : Use an Amazon SageMaker seq2seq algorithm to translate from Spanish to English, if necessary. Use a SageMaker Latent Dirichlet Allocation (LDA) algorithm to find the topics.
  
  C : Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics.
  
  D : Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Lex to extract topics form the content.
  
  E : Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics.
  
  Answer: C,E
Question 4
- An online store is predicting future book sales by using a linear regression model that is based on past sales data. The data includes duration, a numerical feature that represents the number of days that a book has been listed in the online store. A data scientist performs an exploratory data analysis and discovers that the relationship between book sales and duration is skewed and non-linear.
  Which data transformation step should the data scientist take to improve the predictions of the model?
  A : One-hot encoding
  
  B : Cartesian product transformation
  
  C : Quantile binning
  
  D : Normalization
  
  Answer: C
Question 5
- A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.
  Which storage scheme is MOST adapted to this scenario?
  
  A : Store datasets as files in Amazon S3.
  
  B : Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
  
  C : Store datasets as tables in a multi-node Amazon Redshift cluster.
  
  D : Store datasets as global tables in Amazon DynamoDB.
  
  Answer: A

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