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NEW QUESTION: 1
Refer to the exhibit.

What is the reason that the two devices failed to form an EIGRP neighbor relationship?
A. The hold timers are mismatched
B. The advertised MD5 digests do not match between the devices.
C. The K-values are invalid
D. The two devices have different key IDs
Answer: D

NEW QUESTION: 2
During FTTx networking and data planning, consider the safety of users, which of the following measures can prevent the attack among users?
A. defense IP spoofing
B. use anti-mac to solve
C. user Layer 2 isolation and controlled visits
D. use physical port information to identify the user
Answer: C

NEW QUESTION: 3
Refer to the exhibit.



Boot from SAN fails. The host fails to detect the LUNs. You must resolve the issue. Drag and drop the WWPNs on the left to the correct zones on the right.

Answer:
Explanation:

NEW QUESTION: 4
Note: This question is part of a series of questions that present the same scenario. Each ques-tion on the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You need to design the system that handles uploaded documents.
Solution: Use an Azure Data Lake Store as the location to upload documents. Use Azure Event Grid for user notification and to start processing.
Does the solution meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Explanation/Reference:
Explanation:
An Azure Blob Container, which uses an object store with flat namespace, is better than an Azure Data Lake Store, which uses Azure Data Lake uses an Hierarchical file system.
Scenario: Document Uploads
During the document upload process, the solution must capture information about the geographic location where documents originate. Processing of documents must be automatically triggered when documents are uploaded. Customers must be notified when analysis of their uploaded documents begins.
Uploaded documents must be processed using Azure Machine Learning Studio in an Azure Data Factory pipeline. The machine learning portion of the pipeline is updated once a quarter.
When document processing is complete, the documents and the results of the analysis process must be visible.
Reference: https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-comparison-with-blob- storage