Cisco 200-901 Exam With Confidence Using Practice Dumps

The Bash script shown performs the following actions:

It reads a directory name into the variable ndir.

It enters a loop that continues as long as the directory specified by ndir exists (-d "$ndir" checks if a directory exists).

Inside the loop, it changes the current directory to ndir.

Once the loop exits (when a directory with the name ndir no longer exists), it creates a new directory with the name stored in ndir.

Thus, the script ensures that it continues to navigate into directories with the given name until it finds one that does not exist, and then creates it.

When converting XML to JSON, the structure should maintain the hierarchical organization and data representation. The provided XML structure:

10.00

9.00

8.00

20.00

12.00

translates to the following JSON structure:

{

"books": {

"science": {

"biology": "10.00",

"geology": "9.00",

"chemistry": "8.00"

},

"math": {

"calculus": "20.00",

"algebra": "12.00"

}

}

}

Root Element: The root element translates to a key "books".

Nested Elements: The and $elements\; become\; nested\; objects\; under\; "books".$

Leaf Elements: The individual subjects and their prices (e.g., 10.00) become key-value pairs within their respective parent objects.

Option A:

json

Copy code

{

"books": {

"science": {

"biology": "10.00",

"geology": "9.00",

"chemistry": "8.00"

},

"math": {

"calculus": "20.00",

"algebra": "12.00"

}

}

}

This option accurately represents the hierarchical structure of the XML data, making it the correct equivalent.

Option B, C, and D:

These options incorrectly use array notation for the nested objects, which is not consistent with the original XML structure. The XML structure indicates that "science" and "math" are single objects with multiple key-value pairs, not arrays.

References:

Converting Between XML and JSON

JSON and XML Comparison

Edge computing processes data locally, close to where it is generated, rather than sending it to a centralized cloud server. This approach enhances privacy control and reduces latency.

Local Processing: Sensitive data is analyzed locally on edge devices, reducing the need to transmit potentially sensitive information over the network.

Privacy Control: By keeping data processing at the edge, organizations can better comply with data privacy regulations and reduce the risk of data breaches.

Reduced Latency: Local data processing minimizes the delay associated with sending data to and from the cloud, improving real-time analysis capabilities.

References:

Edge Computing and Data Privacy: Edge Computing Benefits