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NEW QUESTION 45
Refer to the exhibit.
An organization uses one specific CloudHub (AWS) region for all CloudHub deployments.
How are CloudHub workers assigned to availability zones (AZs) when the organization’s Mule applications are deployed to CloudHub in that region?
- A. Workers are randomly distributed across available AZs within that region
- B. AZs are selected as part of the Mule application’s deployment configuration
- C. An AZ is randomly selected for a Mule application, and all the Mule application’s CloudHub workers are assigned to that one AZ
- D. Workers belonging to a given environment are assigned to the same AZ within that region
Answer: C
NEW QUESTION 46
When designing an upstream API and its implementation, the development team has been advised to NOT set timeouts when invoking a downstream API, because that downstream API has no SLA that can be relied upon. This is the only downstream API dependency of that upstream API.
Assume the downstream API runs uninterrupted without crashing. What is the impact of this advice?
- A. A toad-dependent timeout of less than 1000 ms will be applied by the Mule runtime in which the downstream API implementation executes
- B. A default timeout of 500 ms will automatically be applied by the Mule runtime in which the upstream API implementation executes
- C. The invocation of the downstream API will run to completion without timing out
- D. An SLA for the upstream API CANNOT be provided
Answer: D
Explanation:
Correct answer: An SLA for the upstream API CANNOT be provided.
*****************************************
>> First thing first, the default HTTP response timeout for HTTP connector is 10000 ms (10 seconds). NOT 500 ms.
>> Mule runtime does NOT apply any such “load-dependent” timeouts. There is no such behavior currently in Mule.
>> As there is default 10000 ms time out for HTTP connector, we CANNOT always guarantee that the invocation of the downstream API will run to completion without timing out due to its unreliable SLA times. If the response time crosses 10 seconds then the request may time out.
The main impact due to this is that a proper SLA for the upstream API CANNOT be provided.
NEW QUESTION 47
A retail company with thousands of stores has an API to receive data about purchases and insert it into a single database. Each individual store sends a batch of purchase data to the API about every 30 minutes. The API implementation uses a database bulk insert command to submit all the purchase data to a database using a custom JDBC driver provided by a data analytics solution provider. The API implementation is deployed to a single CloudHub worker. The JDBC driver processes the data into a set of several temporary disk files on the CloudHub worker, and then the data is sent to an analytics engine using a proprietary protocol. This process usually takes less than a few minutes. Sometimes a request fails. In this case, the logs show a message from the JDBC driver indicating an out-of-file-space message. When the request is resubmitted, it is successful. What is the best way to try to resolve this throughput issue?
- A. se a CloudHub autoscaling policy to add CloudHub workers
- B. Increase the number of CloudHub workers
- C. Use a CloudHub autoscaling policy to increase the size of the CloudHub worker
- D. Increase the size of the CloudHub worker(s)
Answer: B
Explanation:
Correct answer: Increase the size of the CloudHub worker(s)
*****************************************
The key details that we can take out from the given scenario are:
>> API implementation uses a database bulk insert command to submit all the purchase data to a database
>> JDBC driver processes the data into a set of several temporary disk files on the CloudHub worker
>> Sometimes a request fails and the logs show a message indicating an out-of-file-space message Based on above details:
>> Both auto-scaling options does NOT help because we cannot set auto-scaling rules based on error messages. Auto-scaling rules are kicked-off based on CPU/Memory usages and not due to some given error or disk space issues.
>> Increasing the number of CloudHub workers also does NOT help here because the reason for the failure is not due to performance aspects w.r.t CPU or Memory. It is due to disk-space.
>> Moreover, the API is doing bulk insert to submit the received batch data. Which means, all data is handled by ONE worker only at a time. So, the disk space issue should be tackled on “per worker” basis. Having multiple workers does not help as the batch may still fail on any worker when disk is out of space on that particular worker.
Therefore, the right way to deal this issue and resolve this is to increase the vCore size of the worker so that a new worker with more disk space will be provisioned.
NEW QUESTION 48
What API policy would LEAST likely be applied to a Process API?
- A. Custom circuit breaker
- B. Client ID enforcement
- C. JSON threat protection
- D. Rate limiting
Answer: C
NEW QUESTION 49
Select the correct Owner-Layer combinations from below options
- A. 1. App Developers owns and focuses on Experience Layer APIs
2. LOB IT owns and focuses on Process Layer APIs
3. Central IT owns and focuses on System Layer APIs - B. 1. Central IT owns and focuses on Experience Layer APIs
2. LOB IT owns and focuses on Process Layer APIs
3. App Developers owns and focuses on System Layer APIs - C. 1. App Developers owns and focuses on Experience Layer APIs
2. Central IT owns and focuses on Process Layer APIs
3. LOB IT owns and focuses on System Layer APIs
Answer: A
Explanation:
Correct answer:
1. App Developers owns and focuses on Experience Layer APIs
2. LOB IT owns and focuses on Process Layer APIs
3. Central IT owns and focuses on System Layer APIs
References:
https://blogs.mulesoft.com/biz/api/experience-api-ownership/
https://blogs.mulesoft.com/biz/api/process-api-ownership/
https://blogs.mulesoft.com/biz/api/system-api-ownership/
NEW QUESTION 50
……
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