4 Production environment is running on a dedicated Virtual Private Cloud (VPC) on CloudHub 1,0, and the security team guidelines clearly state no traffic on HTTP. Which two options support these security guidelines?
A. Option A
B. Option B
C. Option C
D. Option D
E. Option E
An API implementation is deployed to CloudHub. What conditions can be alerted on using the default Anypoint Platform functionality, where the alert conditions depend on the API invocations to an API implementation?
A. When the API invocations are sent directly to the internal DNS record of the API implementation
B. When the API invocations are not over-a- secure TLS/SSL communication channel
C. When the APL invecations originate from a geography different than the API
D. When the number of API invocations are below a threshold
What do the API invocation metrics provided by Anypoint Platform provide?
A.
ROI metrics from APIs that can be directly shared with business users
B.
Measurements of the effectiveness of the application network based on the level of reuse
C.
Data on past API invocations to help identify anomalies and usage patterns across various APIs
D.
Proactive identification of likely future policy violations that exceed a given threat
threshold
Data on past API invocations to help identify anomalies and usage patterns across various APIs
Explanation: Explanation
Correct Answer: Data on past API invocations to help identify anomalies and usage
patterns across various APIs
*****************************************
API Invocation metrics provided by Anypoint Platform:
>> Does NOT provide any Return Of Investment (ROI) related information. So the option
suggesting it is OUT.
>> Does NOT provide any information w.r.t how APIs are reused, whether there is effective
usage of APIs or not etc...
>> Does NOT prodive any prediction information as such to help us proactively identify any
future policy violations.
So, the kind of data/information we can get from such metrics is on past API invocations to
help identify anomalies and usage patterns across various APIs.
Reference:
https://usermanual.wiki/Document/APAAppNetstudentManual02may2018.991784750.pdf
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.
Use a CloudHub autoscaling policy to increase the size of the CloudHub worker
C.
Increase the size of the CloudHub worker(s)
D.
Increase the number of CloudHub workers
Increase the number of CloudHub workers
Explanation: 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.
A business process is being implemented within an organization's application network. The architecture group proposes using a more coarse-grained application network design with relatively fewer APIs deployed to the application network compared to a more fine-grained design. Overall, which factor typically increases with a more coarse-grained design for this business process implementation and deployment compared with using a more finegrained design?
A. The complexity of each API implementation
B. The number of discoverable assets related to APIs deployed in the application network
C. The number of possible connections between API implementations in the application network
D. The usage of network infrastructure resources by the application network
A Rate Limiting policy is applied to an API implementation to protect the back-end system. Recently, there have been surges in demand that cause some API client POST requests to the API implementation to be rejected with policy-related errors, causing delays and complications to the API clients. How should the API policies that are applied to the API implementation be changed to reduce the frequency of errors returned to API clients, while still protecting the back-end system?
A. Keep the Rate Limiting policy and add 9 Client ID Enforcement policy
B. Remove the Rate Limiting policy and add an HTTP Caching policy
C. Remove the Rate Limiting policy and add a Spike Control policy
D. Keep the Rate Limiting policy and add an SLA-based Spike Control policy
Explanation:
When managing high traffic to an API, especially with POST requests, it is
crucial to ensure the API’s policies both protect the back-end systems and provide a
smooth client experience. Here’s the approach to reducing errors:
Rate Limiting Policy: This policy enforces a limit on the number of requests within
a defined time period. However, rate limiting alone may cause clients to hit limits
during demand surges, leading to errors.
An API implementation is being designed that must invoke an Order API, which is known to
repeatedly experience downtime.
For this reason, a fallback API is to be called when the Order API is unavailable.
What approach to designing the invocation of the fallback API provides the best resilience?
A.
Search Anypoint Exchange for a suitable existing fallback API, and then implement
invocations to this fallback API in addition to the Order API
B.
Create a separate entry for the Order API in API Manager, and then invoke this API as a
fallback API if the primary Order API is unavailable
C.
Redirect client requests through an HTTP 307 Temporary Redirect status code to the
fallback API whenever the Order API is unavailable
D.
Set an option in the HTTP Requester component that invokes the Order API to instead
invoke a fallback API whenever an HTTP 4xx or 5xx response status code is returned from
the Order API
Search Anypoint Exchange for a suitable existing fallback API, and then implement
invocations to this fallback API in addition to the Order API
Explanation: Explanation
Correct Answer: Search Anypoint exchange for a suitable existing fallback API, and then
implement invocations to this fallback API in addition to the order API
*****************************************
>> It is not ideal and good approach, until unless there is a pre-approved agreement with
the API clients that they will receive a HTTP 3xx temporary redirect status code and they
have to implement fallback logic their side to call another API.
>> Creating separate entry of same Order API in API manager would just create an
another instance of it on top of same API implementation. So, it does NO GOOD by using
clone od same API as a fallback API. Fallback API should be ideally a different API
implementation that is not same as primary one.
>> There is NO option currently provided by Anypoint HTTP Connector that allows us to
invoke a fallback API when we receive certain HTTP status codes in response.
The only statement TRUE in the given options is to Search Anypoint exchange for a
suitable existing fallback API, and then implement invocations to this fallback API in
addition to the order API.
An organization has created an API-led architecture that uses various API layers to integrate mobile clients with a backend system. The backend system consists of a number of specialized components and can be accessed via a REST API. The process and
experience APIs share the same bounded-context model that is different from the backend
data model. What additional canonical models, bounded-context models, or anti-corruption
layers are best added to this architecture to help process data consumed from the backend
system?
A.
Create a bounded-context model for every layer and overlap them when the boundary
contexts overlap, letting API developers know about the differences between upstream and
downstream data models
B.
Create a canonical model that combines the backend and API-led models to simplify
and unify data models, and minimize data transformations.
C.
Create a bounded-context model for the system layer to closely match the backend data
model, and add an anti-corruption layer to let the different bounded contexts cooperate
across the system and process layers
D.
Create an anti-corruption layer for every API to perform transformation for every data
model to match each other, and let data simply travel between APIs to avoid the complexity
and overhead of building canonical models
Create a bounded-context model for the system layer to closely match the backend data
model, and add an anti-corruption layer to let the different bounded contexts cooperate
across the system and process layers
Explanation: Explanation
Correct Answer: Create a bounded-context model for the system layer to closely match the
backend data model, and add an anti-corruption layer to let the different bounded contexts
cooperate across the system and process layers
*****************************************
>> Canonical models are not an option here as the organization has already put in efforts
and created bounded-context models for Experience and Process APIs.
>> Anti-corruption layers for ALL APIs is unnecessary and invalid because it is mentioned
that experience and process APIs share same bounded-context model. It is just the System
layer APIs that need to choose their approach now.
>> So, having an anti-corruption layer just between the process and system layers will work
well. Also to speed up the approach, system APIs can mimic the backend system data
model.
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