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NEW QUESTION 33
What is most likely NOT a characteristic of an integration test for a REST API implementation?
- A. The test runs immediately after the Mule application has been compiled and packaged
- B. The test prepares a known request payload and validates the response payload
- C. The test needs all source and/or target systems configured and accessible
- D. The test is triggered by an external HTTP request
Answer: A
Explanation:
The test runs immediately after the Mule application has been compiled and packaged
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>> Integration tests are the last layer of tests we need to add to be fully covered.
>> These tests actually run against Mule running with your full configuration in place and are tested from external source as they work in PROD.
>> These tests exercise the application as a whole with actual transports enabled. So, external systems are affected when these tests run.
So, these tests do NOT run immediately after the Mule application has been compiled and packaged.
FYI... Unit Tests are the one that run immediately after the Mule application has been compiled and packaged.
NEW QUESTION 34
Refer to the exhibit.
what is true when using customer-hosted Mule runtimes with the MuleSoft-hosted Anypoint Platform control plane (hybrid deployment)?
- A. The MuleSoft-hosted Shared Load Balancer can be used to load balance API invocations to the Mule runtimes
- B. API implementations can run successfully in customer-hosted Mule runtimes, even when they are unable to communicate with the control plane
- C. Anypoint Runtime Manager initiates a network connection to a Mule runtime in order to deploy Mule applications
- D. Anypoint Runtime Manager automatically ensures HA in the control plane by creating a new Mule runtime instance in case of a node failure
Answer: B
Explanation:
API implementations can run successfully in customer-hosted Mule runtimes, even when they are unable to communicate with the control plane.
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>> We CANNOT use Shared Load balancer to load balance APIs on customer hosted runtimes
>> For Hybrid deployment models, the on-premises are first connected to Runtime Manager using Runtime Manager agent. So, the connection is initiated first from On-premises to Runtime Manager. Then all control can be done from Runtime Manager.
>> Anypoint Runtime Manager CANNOT ensure automatic HA. Clusters/Server Groups etc should be configured before hand.
Only TRUE statement in the given choices is, API implementations can run successfully in customer-hosted Mule runtimes, even when they are unable to communicate with the control plane. There are several references below to justify this statement.
References:
https://docs.mulesoft.com/runtime-manager/deployment-strategies#hybrid-deployments
https://help.mulesoft.com/s/article/On-Premise-Runtimes-Disconnected-From-US-Control-Plane-June-18th-2018
https://help.mulesoft.com/s/article/Runtime-Manager-cannot-manage-On-Prem-Applications-and-Servers-from-U
https://help.mulesoft.com/s/article/On-premise-Runtimes-Appear-Disconnected-in-Runtime-Manager-May-29th-
NEW QUESTION 35
Refer to the exhibit.
What is the best way to decompose one end-to-end business process into a collaboration of Experience, Process, and System APIs?
A) Handle customizations for the end-user application at the Process API level rather than the Experience API level
B) Allow System APIs to return data that is NOT currently required by the identified Process or Experience APIs
C) Always use a tiered approach by creating exactly one API for each of the 3 layers (Experience, Process and System APIs)
D) Use a Process API to orchestrate calls to multiple System APIs, but NOT to other Process APIs
- A. Option B
- B. Option D
- C. Option A
- D. Option C
Answer: A
Explanation:
Allow System APIs to return data that is NOT currently required by the identified Process or Experience APIs.
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>> All customizations for the end-user application should be handled in "Experience API" only. Not in Process API
>> We should use tiered approach but NOT always by creating exactly one API for each of the 3 layers.
Experience APIs might be one but Process APIs and System APIs are often more than one. System APIs for sure will be more than one all the time as they are the smallest modular APIs built in front of end systems.
>> Process APIs can call System APIs as well as other Process APIs. There is no such anti-design pattern in API-Led connectivity saying Process APIs should not call other Process APIs.
So, the right answer in the given set of options that makes sense as per API-Led connectivity principles is to allow System APIs to return data that is NOT currently required by the identified Process or Experience APIs.
This way, some future Process APIs can make use of that data from System APIs and we need NOT touch the System layer APIs again and again.
NEW QUESTION 36
What API policy would be LEAST LIKELY used when designing an Experience API that is intended to work with a consumer mobile phone or tablet application?
- A. OAuth 2.0 access token enforcement
- B. IPwhitellst
- C. JSON threat protection
- D. Client ID enforcement
Answer: B
Explanation:
IP whitelist
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>> OAuth 2.0 access token and Client ID enforcement policies are VERY common to apply on Experience APIs as API consumers need to register and access the APIs using one of these mechanisms
>> JSON threat protection is also VERY common policy to apply on Experience APIs to prevent bad or suspicious payloads hitting the API implementations.
>> IP whitelisting policy is usually very common in Process and System APIs to only whitelist the IP range inside the local VPC. But also applied occassionally on some experience APIs where the End User/ API Consumers are FIXED.
>> When we know the API consumers upfront who are going to access certain Experience APIs, then we can request for static IPs from such consumers and whitelist them to prevent anyone else hitting the API.
However, the experience API given in the question/ scenario is intended to work with a consumer mobile phone or tablet application. Which means, there is no way we can know all possible IPs that are to be whitelisted as mobile phones and tablets can so many in number and any device in the city/state/country/globe.
So, It is very LEAST LIKELY to apply IP Whitelisting on such Experience APIs whose consumers are typically Mobile Phones or Tablets.
NEW QUESTION 37
What is typically NOT a function of the APIs created within the framework called API-led connectivity?
- A. They reduce the dependency on the underlying backend systems by helping unlock data from backend systems In a reusable and consumable way.
- B. They allow for innovation at the user Interface level by consuming the underlying assets without being aware of how data Is being extracted from backend systems.
- C. They provide an additional layer of resilience on top of the underlying backend system, thereby insulating clients from extended failure of these systems.
- D. They can compose data from various sources and combine them with orchestration logic to create higher level value.
Answer: C
Explanation:
They provide an additional layer of resilience on top of the underlying backend system, thereby insulating clients from extended failure of these systems.
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In API-led connectivity,
>> Experience APIs - allow for innovation at the user interface level by consuming the underlying assets without being aware of how data is being extracted from backend systems.
>> Process APIs - compose data from various sources and combine them with orchestration logic to create higher level value
>> System APIs - reduce the dependency on the underlying backend systems by helping unlock data from backend systems in a reusable and consumable way.
However, they NEVER promise that they provide an additional layer of resilience on top of the underlying backend system, thereby insulating clients from extended failure of these systems.
https://dzone.com/articles/api-led-connectivity-with-mule
NEW QUESTION 38
A system API is deployed to a primary environment as well as to a disaster recovery (DR) environment, with different DNS names in each environment. A process API is a client to the system API and is being rate limited by the system API, with different limits in each of the environments. The system API's DR environment provides only 20% of the rate limiting offered by the primary environment. What is the best API fault-tolerant invocation strategy to reduce overall errors in the process API, given these conditions and constraints?
- A. Invoke the system API deployed to the primary environment; add timeout and retry logic to the process API to avoid intermittent failures; if it still fails, invoke the system API deployed to the DR environment
- B. Invoke the system API deployed to the primary environment; add timeout and retry logic to the process API to avoid intermittent failures; if it still fails, invoke a copy of the process API deployed to the DR environment
- C. Invoke the system API deployed to the primary environment; add retry logic to the process API to handle intermittent failures by invoking the system API deployed to the DR environment
- D. In parallel, invoke the system API deployed to the primary environment and the system API deployed to the DR environment; add timeout and retry logic to the process API to avoid intermittent failures; add logic to the process API to combine the results
Answer: A
Explanation:
Invoke the system API deployed to the primary environment; add timeout and retry logic to the process API to avoid intermittent failures; if it still fails, invoke the system API deployed to the DR environment
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There is one important consideration to be noted in the question which is - System API in DR environment provides only 20% of the rate limiting offered by the primary environment. So, comparitively, very less calls will be allowed into the DR environment API opposed to its primary environment. With this in mind, lets analyse what is the right and best fault-tolerant invocation strategy.
1. Invoking both the system APIs in parallel is definitely NOT a feasible approach because of the 20% limitation we have on DR environment. Calling in parallel every time would easily and quickly exhaust the rate limits on DR environment and may not give chance to genuine intermittent error scenarios to let in during the time of need.
2. Another option given is suggesting to add timeout and retry logic to process API while invoking primary environment's system API. This is good so far. However, when all retries failed, the option is suggesting to invoke the copy of process API on DR environment which is not right or recommended. Only system API is the one to be considered for fallback and not the whole process API. Process APIs usually have lot of heavy orchestration calling many other APIs which we do not want to repeat again by calling DR's process API. So this option is NOT right.
3. One more option given is suggesting to add the retry (no timeout) logic to process API to directly retry on DR environment's system API instead of retrying the primary environment system API first. This is not at all a proper fallback. A proper fallback should occur only after all retries are performed and exhausted on Primary environment first. But here, the option is suggesting to directly retry fallback API on first failure itself without trying main API. So, this option is NOT right too.
This leaves us one option which is right and best fit.
- Invoke the system API deployed to the primary environment
- Add Timeout and Retry logic on it in process API
- If it fails even after all retries, then invoke the system API deployed to the DR environment.
NEW QUESTION 39
What CANNOT be effectively enforced using an API policy in Anypoint Platform?
- A. Maintaining tamper-proof credentials between APIs
- B. Logging HTTP requests and responses
- C. Backend system overloading
- D. Guarding against Denial of Service attacks
Answer: D
Explanation:
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.
NEW QUESTION 40
What is true about API implementations when dealing with legal regulations that require all data processing to be performed within a certain jurisdiction (such as in the USA or the EU)?
- A. They must ensure ALL data is encrypted both in transit and at rest
- B. They must avoid using the Object Store as it depends on services deployed ONLY to the US East region
- C. They must use a Jurisdiction-local external messaging system such as Active MQ rather than Anypoint MQ
- D. They must te deployed to Anypoint Platform runtime planes that are managed by Anypoint Platform control planes, with both planes in the same Jurisdiction
Answer: D
Explanation:
They must be deployed to Anypoint Platform runtime planes that are managed by Anypoint Platform control planes, with both planes in the same Jurisdiction.
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>> As per legal regulations, all data processing to be performed within a certain jurisdiction. Meaning, the data in USA should reside within USA and should not go out. Same way, the data in EU should reside within EU and should not go out.
>> So, just encrypting the data in transit and at rest does not help to be compliant with the rules. We need to make sure that data does not go out too.
>> The data that we are talking here is not just about the messages that are published to Anypoint MQ. It includes the apps running, transaction states, application logs, events, metric info and any other metadata. So, just replacing Anypoint MQ with a locally hosted ActiveMQ does NOT help.
>> The data that we are talking here is not just about the key/value pairs that are stored in Object Store. It includes the messages published, apps running, transaction states, application logs, events, metric info and any other metadata. So, just avoiding using Object Store does NOT help.
>> The only option left and also the right option in the given choices is to deploy application on runtime and control planes that are both within the jurisdiction.
NEW QUESTION 41
Which layer in the API-led connectivity focuses on unlocking key systems, legacy systems, data sources etc and exposes the functionality?
- A. System Layer
- B. Experience Layer
- C. Process Layer
Answer: A
Explanation:
System Layer
The APIs used in an API-led approach to connectivity fall into three categories:
System APIs - these usually access the core systems of record and provide a means of insulating the user from the complexity or any changes to the underlying systems. Once built, many users, can access data without any need to learn the underlying systems and can reuse these APIs in multiple projects.
Process APIs - These APIs interact with and shape data within a single system or across systems (breaking down data silos) and are created here without a dependence on the source systems from which that data originates, as well as the target channels through which that data is delivered.
Experience APIs - Experience APIs are the means by which data can be reconfigured so that it is most easily consumed by its intended audience, all from a common data source, rather than setting up separate point-to-point integrations for each channel. An Experience API is usually created with API-first design principles where the API is designed for the specific user experience in mind.
NEW QUESTION 42
A company has created a successful enterprise data model (EDM). The company is committed to building an application network by adopting modern APIs as a core enabler of the company's IT operating model. At what API tiers (experience, process, system) should the company require reusing the EDM when designing modern API data models?
- A. At the experience and system tiers
- B. At the experience and process tiers
- C. At the process and system tiers
- D. At the experience, process, and system tiers
Answer: C
Explanation:
At the process and system tiers
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>> Experience Layer APIs are modeled and designed exclusively for the end user's experience. So, the data models of experience layer vary based on the nature and type of such API consumer. For example, Mobile consumers will need light-weight data models to transfer with ease on the wire, where as web-based consumers will need detailed data models to render most of the info on web pages, so on. So, enterprise data models fit for the purpose of canonical models but not of good use for experience APIs.
>> That is why, EDMs should be used extensively in process and system tiers but NOT in experience tier.
NEW QUESTION 43
An API has been updated in Anypoint Exchange by its API producer from version 3.1.1 to 3.2.0 following accepted semantic versioning practices and the changes have been communicated via the API's public portal.
The API endpoint does NOT change in the new version.
How should the developer of an API client respond to this change?
- A. The API producer should be requested to run the old version in parallel with the new one
- B. The update should be identified as a project risk and full regression testing of the functionality that uses this API should be run
- C. The API producer should be contacted to understand the change to existing functionality
- D. The API client code ONLY needs to be changed if it needs to take advantage of new features
Answer: D
NEW QUESTION 44
A set of tests must be performed prior to deploying API implementations to a staging environment. Due to data security and access restrictions, untested APIs cannot be granted access to the backend systems, so instead mocked data must be used for these tests. The amount of available mocked data and its contents is sufficient to entirely test the API implementations with no active connections to the backend systems. What type of tests should be used to incorporate this mocked data?
- A. Performance tests
- B. Integration tests
- C. Unit tests (Whitebox)
- D. Functional tests (Blackbox)
Answer: C
Explanation:
Unit tests (Whitebox)
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NEW QUESTION 45
What is a typical result of using a fine-grained rather than a coarse-grained API deployment model to implement a given business process?
- A. A better response time for the end user as a result of the APIs being smaller in scope and complexity
- B. A decrease in the number of connections within the application network supporting the business process
- C. An overall tower usage of resources because each fine-grained API consumes less resources
- D. A higher number of discoverable API-related assets in the application network
Answer: D
Explanation:
A higher number of discoverable API-related assets in the application network.
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>> We do NOT get faster response times in fine-grained approach when compared to coarse-grained approach.
>> In fact, we get faster response times from a network having coarse-grained APIs compared to a network having fine-grained APIs model. The reasons are below.
Fine-grained approach:
1. will have more APIs compared to coarse-grained
2. So, more orchestration needs to be done to achieve a functionality in business process.
3. Which means, lots of API calls to be made. So, more connections will needs to be established. So, obviously more hops, more network i/o, more number of integration points compared to coarse-grained approach where fewer APIs with bulk functionality embedded in them.
4. That is why, because of all these extra hops and added latencies, fine-grained approach will have bit more response times compared to coarse-grained.
5. Not only added latencies and connections, there will be more resources used up in fine-grained approach due to more number of APIs.
That's why, fine-grained APIs are good in a way to expose more number of resuable assets in your network and make them discoverable. However, needs more maintenance, taking care of integration points, connections, resources with a little compromise w.r.t network hops and response times.
NEW QUESTION 46
Traffic is routed through an API proxy to an API implementation. The API proxy is managed by API Manager and the API implementation is deployed to a CloudHub VPC using Runtime Manager. API policies have been applied to this API. In this deployment scenario, at what point are the API policies enforced on incoming API client requests?
- A. At the API proxy
- B. At both the API proxy and the API implementation
- C. At the API implementation
- D. At a MuleSoft-hosted load balancer
Answer: A
Explanation:
At the API proxy
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>> API Policies can be enforced at two places in Mule platform.
>> One - As an Embedded Policy enforcement in the same Mule Runtime where API implementation is running.
>> Two - On an API Proxy sitting in front of the Mule Runtime where API implementation is running.
>> As the deployment scenario in the question has API Proxy involved, the policies will be enforced at the API Proxy.
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. Use a CloudHub autoscaling policy to increase the size of the CloudHub worker
- B. se a CloudHub autoscaling policy to add CloudHub workers
- C. Increase the size of the CloudHub worker(s)
- D. Increase the number of CloudHub workers
Answer: D
Explanation:
Increase the size of the CloudHub worker(s)
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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
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