Amazon Web Services 2cram all Das-c01 Test Inside Amazon Web Services Questions by Colin q94 vce pdf

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Exam Name:	AWS Certified Data Analytics - Specialty
Exam Code:	DAS-C01 Dumps
Vendor:	Amazon Web Services	Certification:	AWS Certified Data Analytics
Questions:	207 Q&A's	Shared By:	colin

Question 28

A banking company wants to collect large volumes of transactional data using Amazon Kinesis Data Streams for real-time analytics. The company usesPutRecord to send data to Amazon Kinesis, and has observed network outages during certain times of the day. The company wants to obtain exactly once semantics for the entire processing pipeline.

What should the company do to obtain these characteristics?

Options:

Design the application so it can remove duplicates during processing be embedding a unique ID in each record.

Rely on the processing semantics of Amazon Kinesis Data Analytics to avoid duplicate processing of events.

Design the data producer so events are not ingested into Kinesis Data Streams multiple times.

Rely on the exactly one processing semantics of Apache Flink and Apache Spark Streaming included in Amazon EMR.

Discussion

Question 29

A company receives datasets from partners at various frequencies. The datasets include baseline data and incremental data. The company needs to merge and store all the datasets without reprocessing the data.

Which solution will meet these requirements with the LEAST development effort?

Options:

Use an AWS Glue job with a temporary table to process the datasets. Store the data in an Amazon RDS table.

Use an Apache Spark job in an Amazon EMR cluster to process the datasets. Store the data in EMR File System (EMRFS).

Use an AWS Glue job with job bookmarks enabled to process the datasets. Store the data in Amazon S3.

Use an AWS Lambda function to process the datasets. Store the data in Amazon S3.

Discussion

Question 30

A company is building a data lake and needs to ingest data from a relational database that has time-series data. The company wants to use managed services to accomplish this. The process needs to be scheduled daily and bring incremental data only from the source into Amazon S3.

What is the MOST cost-effective approach to meet these requirements?

Options:

Use AWS Glue to connect to the data source using JDBC Drivers. Ingest incremental records only using job bookmarks.

Use AWS Glue to connect to the data source using JDBC Drivers. Store the last updated key in an Amazon DynamoDB table and ingest the data using the updated key as a filter.

Use AWS Glue to connect to the data source using JDBC Drivers and ingest the entire dataset. Use appropriate Apache Spark libraries to compare the dataset, and find the delta.

Use AWS Glue to connect to the data source using JDBC Drivers and ingest the full data. Use AWS DataSync to ensure the delta only is written into Amazon S3.

Discussion

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Question 31

A company uses Amazon Redshift as its data warehouse. A new table has columns that contain sensitive data. The data in the table will eventually be referenced by several existing queries that run many times a day.

A data analyst needs to load 100 billion rows of data into the new table. Before doing so, the data analyst must ensure that only members of the auditing group can read the columns containing sensitive data.

How can the data analyst meet these requirements with the lowest maintenance overhead?

Options:

Load all the data into the new table and grant the auditing group permission to read from the table. Load all the data except for the columns containing sensitive data into a second table. Grant the appropriate users read-only permissions to the second table.

Load all the data into the new table and grant the auditing group permission to read from the table. Use the GRANT SQL command to allow read-only access to a subset of columns to the appropriate users.

Load all the data into the new table and grant all users read-only permissions to non-sensitive columns. Attach an IAM policy to the auditing group with explicit ALLOW access to the sensitive data columns.

Load all the data into the new table and grant the auditing group permission to read from the table. Create a view of the new table that contains all the columns, except for those considered sensitive, and grant the appropriate users read-only permissions to the table.