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NEW QUESTION: 1
This error is generated when attempting to import a certificate into a vRealize Automation appliance:
Certificate for <vra.vclass.local> doesn't match common name of the certificate subject: 192.168.5.112
Which option is a possible cause?
A. The fully qualified domain namevra.vclass.local doesn't resolve to 192.168.5.112.
B. The IP address of the appliance has been changed to 192.168.5.112.
C. The certificate was issued for the IP address instead of the hostname.
D. A PTR record for 192.168.5.112 is missing in DNS.
Answer: C

NEW QUESTION: 2
You are designing a data warehouse that will be clustered across four servers. Several of the tables in the data warehouse contain transient data that you can rebuild as needed.
The schema for the data changes periodically. You must minimize the work required to make schema changes and deploy those changes across the server farm. Administrators must be able to make the following schema changes:
* Adding columns to tables
* Removing columns from tables
* Adding tables
* Removing tables
Changes to the transient data are done as singleton operations. You must make the data available on all the servers in the server farm. Data movement between servers must occur in real time or near real time.
You must be able to run SELECT statements against the data from any server at any time.
You need to configure the environment.
Which technologies should you implement? To answer, select the appropriate technologies in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: Durable in-Memory OLTP
Databases containing memory-optimized tables, with or without native compiled stored procedures, are fully supported with Always On Availability Groups. There is no difference in the configuration and support for databases which contain In-Memory OLTP objects as compared to those without.
Box 2: Always on Availability groups
The Always On availability groups feature is a high-availability and disaster-recovery solution that provides an enterprise-level alternative to database mirroring. Always On availability groups maximizes the availability of a set of user databases for an enterprise. An availability group supports a failover environment for a discrete set of user databases, known as availability databases, that fail over together. An availability group supports a set of read-write primary databases and one to eight sets of corresponding secondary databases. Optionally, secondary databases can be made available for read-only access and/or some backup operations.
An availability group fails over at the level of an availability replica.
References:
https://docs.microsoft.com/en-us/sql/relational-databases/in-memory-oltp/high-availability-support-for-in-memo
https://docs.microsoft.com/en-us/sql/database-engine/availability-groups/windows/always-on-availability-groups

NEW QUESTION: 3
Welche der folgenden Ansichten und Ergebnisse bieten Profit-Center-Buchhaltungsberichte in New GL? (Wählen Sie drei aus)
A. Bilanz
B. Varianzkategorien der Produktion
C. Kosten der verkauften Waren
D. Teile mit festen und variablen Kosten
E. Kapitalrendite
Answer: A,C,E

NEW QUESTION: 4
You have been asked to select the storage system for the click-data of your company's large portfolio of websites. This data is streamed in from a custom website analytics package at a typical rate of 6,000 clicks per minute, with bursts of up to 8,500 clicks per second. It must been stored for future analysis by your data science and user experience teams. Which storage infrastructure should you choose?
A. Google Cloud SQL
B. Google Cloud Storage
C. Google Cloud Bigtable
D. Google cloud Datastore
Answer: C
Explanation:
Explanation
https://cloud.google.com/solutions/data-analytics-partner-ecosystem
https://zulily-tech.com/2015/08/10/leveraging-google-cloud-dataflow-for-clickstream-processing/ Google Cloud Bigtable is a scalable, fully-managed NoSQL wide-column database that is suitable for both real-time access and analytics workloads.
Good for:
* Low-latency read/write access
* High-throughput analytics
* Native time series support
* Common workloads:
* IoT, finance, adtech
* Personalization, recommendations
* Monitoring
* Geospatial datasets
* Graphs