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NEW QUESTION: 1
A customer owns a MySQL database that is accessed by various clients who expect, at most, 100 ms latency on requests. Once a record is stored in the database, it rarely changed. Clients only access one record at a time.
Database access has been increasing exponentially due to increased client demand. The resultant load will soon exceed the capacity of the most expensive hardware available for purchase. The customer wants to migrate to AWS, and is willing to change database systems.
Which service would alleviate the database load issue and offer virtually unlimited scalability for the future?
A. Amazon DynamoDB
B. AWS Data Pipeline
C. Amazon Redshift
D. Amazon RDS
Answer: A
Explanation:
Explanation
Many companies consider migrating from relational databases like MySQL to Amazon DynamoDB, a fully managed, fast, highly scalable, and flexible NoSQL database service. For example, DynamoDB can increase or decrease capacity based on traffic, in accordance with business needs. The total cost of servicing can be optimized more easily than for the typical media-based RDBMS.
https://aws.amazon.com/blogs/big-data/near-zero-downtime-migration-from-mysql-to-dynamodb/
NEW QUESTION: 2
Clark is a professional hacker. He created and configured multiple domains pointing to the same host to switch quickly between the domains and avoid detection.
Identify the behavior of the adversary In the above scenario.
A. Data staging
B. use of command-line interface
C. Use of DNS tunneling
D. Unspecified proxy activities
Answer: B
NEW QUESTION: 3
You are developing a SQL Server Integration Services (SSIS) package.
The package uses a data flow task to source data from a SQL Server database for loading into a dimension table in a data warehouse.
You need to create a separate data flow path for data that has been modified since it was last processed.
Which two data flow components should you use to identify modified data? (Each correct answer presents a complete solution. Choose all that apply.)
A. Slowly Changing Dimension
B. Lookup
C. Aggregate
D. Multicast
E. Data Conversion
Answer: A,B
Explanation:
Explanation/Reference:
Explanation:
C: Lookup Transformation
The transformation that looks up values in a reference table using an exact match.
D: Slowly Changing Dimension
The Slowly Changing Dimension transformation provides the following functionality for managing slowly changing dimensions:
Matching incoming rows with rows in the lookup table to identify new and existing rows.
Identifying incoming rows that contain changes when changes are not permitted.
Identifying inferred member records that require updating.
Identifying incoming rows that contain historical changes that require insertion of new records and the
updating of expired records.
Detecting incoming rows that contain changes that require the updating of existing records, including
expired ones.
Note:
* SQL Server Integration Services provides three different types of data flow components: sources, transformations, and destinations. Sources extract data from data stores such as tables and views in relational databases, files, and Analysis Services databases. Transformations modify, summarize, and clean data. Destinations load data into data stores or create in-memory datasets.
References:
https://docs.microsoft.com/en-us/sql/integration-services/data-flow/transformations/lookup-transformation
https://docs.microsoft.com/en-us/sql/integration-services/data-flow/transformations/slowly-changing- dimension-transformation