Salesforce MuleSoft-Platform-Architect-I Test Dumps

A.   Guarding against Denial of Service attacks

Answer DescriptionExplanation:

Correct Answer: Guarding against Denial of Service attacks

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>> Backend system overloading can be handled by enforcing "Spike Control Policy"

>> Logging HTTP requests and responses can be done by enforcing "Message Logging Policy"

>> Credentials can be tamper-proofed using "Security" and "Compliance" Policies However, unfortunately, there is no proper way currently on Anypoint Platform to guard against DOS attacks.

Reference: https://help.mulesoft.com/s/article/DDos-Dos-at

A.   Use an SLA-based rate limiting policy and assign a client application to a matching SLA tier based on its type

Answer DescriptionExplanation:

Correct Answer: Use an SLA-based rate limiting policy and assign a client application to a matching SLA tier based on its type.

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>> SLA tiers will come into play whenever any limits to be imposed on APIs based on client type

Reference: https://docs.mulesoft.com/api-manager/2.x/rate-limiting-and-throttling-slabased-policies

C.   Persistent Object Store

Answer DescriptionExplanation:

Correct Answer: Persistent Object Store

***************************************** >> Redis distributed cache is performant but NOT out-of-the-box solution in Anypoint Platform

>> File-storage is neither performant nor out-of-the-box solution in Anypoint Platform

>> java.util.WeakHashMap needs a completely custom implementation of cache from scratch using Java code and is limited to the JVM where it is running. Which means the state in the cache is not worker aware when running on multiple workers. This type of cache is local to the worker. So, this is neither out-of-the-box nor worker-aware among multiple workers on cloudhub. https://www.baeldung.com/java-weakhashmap

>> Persistent Object Store is an out-of-the-box solution provided by Anypoint Platform which is performant as well as worker aware among multiple workers running on CloudHub. https://docs.mulesoft.com/object-store/ So, Persistent Object Store is the right answer.

C.    Implement an anti-corruption layer in the Order API that transforms the Product API data model into internal data types of the Order API

Answer DescriptionExplanation

Correct Answer: Convince the development team of the product API to adopt the API data model of the Order API such that integration logic of the Order API can work with one consistent internal data model

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Key details to note from the given scenario:

>> Power relationship between Order API and Product API is customer/supplier So, as per below rules of "Power Relationships", the caller (in this case Order API) would request for features to the called (Product API team) and the Product API team would need to accomodate those requests

B.   Set a timeout of 100 ms; that leaves 400 ms for the other two downstream APIs to complete

Answer DescriptionExplanation

Correct Answer: 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.