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NEW QUESTION 45
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. Use a CloudHub autoscaling policy to increase the size of the CloudHub worker
• B. Increase the number of CloudHub workers
• C. Increase the size of the CloudHub worker(s)
• D. se a CloudHub autoscaling policy to add CloudHub workers

Answer: B

Explanation:
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 46
Mule applications that implement a number of REST APIs are deployed to their own subnet that is inaccessible from outside the organization.
External business-partners need to access these APIs, which are only allowed to be invoked from a separate subnet dedicated to partners - called Partner-subnet. This subnet is accessible from the public internet, which allows these external partners to reach it.
Anypoint Platform and Mule runtimes are already deployed in Partner-subnet. These Mule runtimes can already access the APIs.
What is the most resource-efficient solution to comply with these requirements, while having the least impact on other applications that are currently using the APIs?

• A. Implement (or generate) an API proxy Mule application for each of the APIs, then deploy the API proxies to the Mule runtimes
• B. Duplicate the APIs as Mule applications, then deploy them to the Mule runtimes
• C. Redeploy the API implementations to the same servers running the Mule runtimes
• D. Add an additional endpoint to each API for partner-enablement consumption

Answer: A

NEW QUESTION 47
A new upstream API Is being designed to offer an SLA of 500 ms median and 800 ms maximum (99th percentile) response time. The corresponding API implementation needs to sequentially invoke 3 downstream APIs of very similar complexity.
The first of these downstream APIs offers the following SLA for its response time: median: 100 ms, 80th percentile: 500 ms, 95th percentile: 1000 ms.
If possible, how can a timeout be set in the upstream API for the invocation of the first downstream API to meet the new upstream API's desired SLA?

• A. Set a timeout of 50 ms; this times out more invocations of that API but gives additional room for retries
• B. Do not set a timeout; the Invocation of this API Is mandatory and so we must wait until it responds
• C. Set a timeout of 100 ms; that leaves 400 ms for the other two downstream APIs to complete
• D. No timeout is possible to meet the upstream API's desired SLA; a different SLA must be negotiated with the first downstream API or invoke an alternative API

Answer: C

Explanation:
Set a timeout of 100ms; that leaves 400ms for other two downstream APIs to complete
*****************************************
Key details to take from the given scenario:
>> Upstream API's designed SLA is 500ms (median). Lets ignore maximum SLA response times.
>> This API calls 3 downstream APIs sequentially and all these are of similar complexity.
>> The first downstream API is offering median SLA of 100ms, 80th percentile: 500ms; 95th percentile:
1000ms.
Based on the above details:
>> We can rule out the option which is suggesting to set 50ms timeout. Because, if the median SLA itself being offered is 100ms then most of the calls are going to timeout and time gets wasted in retried them and eventually gets exhausted with all retries. Even if some retries gets successful, the remaining time wont leave enough room for 2nd and 3rd downstream APIs to respond within time.
>> The option suggesting to NOT set a timeout as the invocation of this API is mandatory and so we must wait until it responds is silly. As not setting time out would go against the good implementation pattern and moreover if the first API is not responding within its offered median SLA 100ms then most probably it would either respond in 500ms (80th percentile) or 1000ms (95th percentile). In BOTH cases, getting a successful response from 1st downstream API does NO GOOD because already by this time the Upstream API SLA of
500 ms is breached. There is no time left to call 2nd and 3rd downstream APIs.
>> It is NOT true that no timeout is possible to meet the upstream APIs desired SLA.
As 1st downstream API is offering its median SLA of 100ms, it means MOST of the time we would get the responses within that time. So, setting a timeout of 100ms would be ideal for MOST calls as it leaves enough room of 400ms for remaining 2 downstream API calls.

NEW QUESTION 48
An API experiences a high rate of client requests (TPS) vwth small message paytoads. How can usage limits be imposed on the API based on the type of client application?

• A. Use a spike control policy that limits the number of requests for each client application type
• B. Use a cross-origin resource sharing (CORS) policy to limit resource sharing between client applications, configured by the client application type
• C. Use an SLA-based rate limiting policy and assign a client application to a matching SLA tier based on its type
• D. Use a rate limiting policy and a client ID enforcement policy, each configured by the client application type

Answer: C

Explanation:
Use an SLA-based rate limiting policy and assign a client application to a matching SLA tier
based on its type.
*****************************************
>> SLA tiers will come into play whenever any limits to be imposed on APIs based on client type

NEW QUESTION 49
What are 4 important Platform Capabilities offered by Anypoint Platform?

• A. API Versioning, API Runtime Execution and Hosting, API Invocation, API Consumer Engagement
• B. API Design and Development, API Runtime Execution and Hosting, API Versioning, API Deprecation
• C. API Design and Development, API Runtime Execution and Hosting, API Operations and Management, API Consumer Engagement
• D. API Design and Development, API Deprecation, API Versioning, API Consumer Engagement

Answer: C

Explanation:
API Design and Development, API Runtime Execution and Hosting, API Operations and Management, API Consumer Engagement
*****************************************
>> API Design and Development - Anypoint Studio, Anypoint Design Center, Anypoint Connectors
>> API Runtime Execution and Hosting - Mule Runtimes, CloudHub, Runtime Services
>> API Operations and Management - Anypoint API Manager, Anypoint Exchange
>> API Consumer Management - API Contracts, Public Portals, Anypoint Exchange, API Notebooks

NEW QUESTION 50
......