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NEW QUESTION: 1
How are "Rule-based related products" added to the shopping cart when you check their checkbox together with the checkbox for the basic product?
A. As the basic product's custom options
B. As the basic product's configuration options
C. Separately from the basic product
D. As a single grouped product
Answer: C
NEW QUESTION: 2
Which layer of the OSI reference model can ensure reliable end-to-end data transmission( )
A. Session layer
B. Network layer
C. Transport layer
D. Presentation layer
Answer: C
NEW QUESTION: 3
Which three AAA protocols are supported for communication with external AAA providers? (Choose three.)
A. AD
B. ACS
C. RADIUS
D. 802.1X
E. TACACS+
F. LDAP
Answer: C,E,F
Explanation:
Explanation/Reference:
Explanation:
Cisco UCS supports two methods to authenticate user logins: Through user accounts local to Cisco UCS Manager Remotely through one of the following protocols:
LDAP
RADIUS
TACACS+
Reference:
https://www.cisco.com/en/US/docs/unified_computing/ucs/sw/gui/config/guide/141/UCSM_GUI_Confi guration_Guide_141_chapter7.html#concept_95086D8A9B594FD29A3BD8AF5D2DA1FC
NEW QUESTION: 4
You are developing a solution using a Lambda architecture on Microsoft Azure.
The data at test layer must meet the following requirements:
Data storage:
*Serve as a repository (or high volumes of large files in various formats.
*Implement optimized storage for big data analytics workloads.
*Ensure that data can be organized using a hierarchical structure.
Batch processing:
*Use a managed solution for in-memory computation processing.
*Natively support Scala, Python, and R programming languages.
*Provide the ability to resize and terminate the cluster automatically.
Analytical data store:
*Support parallel processing.
*Use columnar storage.
*Support SQL-based languages.
You need to identify the correct technologies to build the Lambda architecture.
Which technologies should you use? To answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Data storage: Azure Data Lake Store
A key mechanism that allows Azure Data Lake Storage Gen2 to provide file system performance at object storage scale and prices is the addition of a hierarchical namespace. This allows the collection of objects/files within an account to be organized into a hierarchy of directories and nested subdirectories in the same way that the file system on your computer is organized. With the hierarchical namespace enabled, a storage account becomes capable of providing the scalability and cost-effectiveness of object storage, with file system semantics that are familiar to analytics engines and frameworks.
Batch processing: HD Insight Spark
Aparch Spark is an open-source, parallel-processing framework that supports in-memory processing to boost the performance of big-data analysis applications.
HDInsight is a managed Hadoop service. Use it deploy and manage Hadoop clusters in Azure. For batch processing, you can use Spark, Hive, Hive LLAP, MapReduce.
Languages: R, Python, Java, Scala, SQL
Analytic data store: SQL Data Warehouse
SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP).
SQL Data Warehouse stores data into relational tables with columnar storage.
References:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch-processing
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-overview-what-is