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NEW QUESTION: 1
Refer to the exhibit.

Which result of running the command is true?
A. All interfaces except M1. M2, and M3 modules are removed from the VDC.
B. All F3 interfaces are removed from the VDC
C. All interfaces except F3 modules are removed from the VDC.
D. All interfaces are reallocated to the VDC
Answer: C

NEW QUESTION: 2

A. Option D
B. Option C
C. Option A
D. Option B
E. Option E
Answer: C,E
Explanation:
Referenceshttps://docs.oracle.com/cd/B28359_01/server.111/b28300/expimp.htm#UPGRD
12560

NEW QUESTION: 3
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

NEW QUESTION: 4
What must be used in Security Policy Rule that contain addresses where NAT policy applies?
A. Post-NAT addresse and Post-Nat zones
B. Pre-NAT addresse and Post-Nat zones
C. Pre-NAT addresse and Pre-NAT zones
D. Post-Nat addresses and Pre-NAT zones
Answer: B
Explanation:
NAT Policy Rule Functionality
Upon ingress, the firewall inspects the packet and does a route lookup to determine the egress interface and zone. Then the firewall determines if the packet matches one of the NAT rules that have been defined, based on source and/or destination zone. It then evaluates and applies any security policies that match the packet based on the original (pre-NAT) source and destination addresses, but the post-NAT zones.
https://www.paloaltonetworks.com/documentation/70/pan-os/pan-os/networking/nat-policy-rules