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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Designing and Sharing APIs | 11% | - Asset sharing and reuse via Anypoint Exchange - API design standards and best practices - API layering: Experience, Process, System APIs - API specification and documentation |
| Topic 2: Architecting and Deploying API Implementations | 11% | - Runtime architecture and deployment options - Networking and security configuration - High availability and fault tolerance - CI/CD and DevOps integration |
| Topic 3: Explaining Application Network Basics | 11% | - API-led connectivity principles - Benefits of modern API design - Core concepts of application networks |
| Topic 4: Applying Integration Patterns | 11% | - Error handling and reliability patterns - Scalability and performance patterns - Common integration patterns and use cases - Event-driven and synchronous integration |
| Topic 5: Deploying API Implementations to CloudHub | 11% | - CloudHub architecture and capabilities - Deployment optimization and scaling - Worker sizing and resource planning - VPC and private space configuration |
| Topic 6: Establishing Organizational and Platform Foundations | 17% | - Anypoint Platform architecture and components - Platform strategy and roadmap definition - Center for Enablement (C4E) operating model - Governance and organizational structure |
| Topic 7: Monitoring and Analyzing Application Networks | 8% | - Logging and alerting configuration - Monitoring strategies and tools - Operational visibility and optimization - Analytics and insight generation |
| Topic 8: Meeting API Quality Goals | 8% | - Performance and latency requirements - Reliability and availability targets - Security and compliance standards - Maintainability and testability |
| Topic 9: Managing APIs | 12% | - API policies and security enforcement - API lifecycle management - Versioning and deprecation strategies - Rate limiting and throttling |
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NEW QUESTION # 29
What do the API invocation metrics provided by Anypoint Platform provide?
Answer: C
Explanation:
Correct Answe r: Data on past API invocations to help identify anomalies and usage patterns across various APIs
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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.
NEW QUESTION # 30
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?
Answer: D
Explanation:
Correct Answe r: Set a timeout of 100ms; that leaves 400ms for other two downstream APIs to complete
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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 # 31
What are the major benefits of MuleSoft proposed IT Operating Model?
Answer: B
Explanation:
Correct Answe r:
1. Decrease the IT delivery gap
2. Meet various business demands without increasing the IT capacity
3. Make consumption of assets at the rate of production.
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NEW QUESTION # 32
Refer to the exhibit. An organization is running a Mule standalone runtime and has configured Active Directory as the Anypoint Platform external Identity Provider. The organization does not have budget for other system components.
What policy should be applied to all instances of APIs in the organization to most effecuvelyKestrict access to a specific group of internal users?
Answer: D
Explanation:
Correct Answe r: Apply a basic authentication - LDAP policy; the internal Active Directory will be configured as the LDAP source for authenticating users.
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>> IP Whitelisting does NOT fit for this purpose. Moreover, the users workstations may not necessarily have static IPs in the network.
>> OAuth 2.0 enforcement requires a client provider which isn't in the organizations system components.
>> It is not an effective approach to let every user create separate client credentials and configure those for their usage.
The effective way it to apply a basic authentication - LDAP policy and the internal Active Directory will be configured as the LDAP source for authenticating users.
NEW QUESTION # 33
Several times a week, an API implementation shows several thousand requests per minute in an Anypoint Monitoring dashboard, Between these bursts, the dashboard shows between two and five requests per minute. The API implementation is running on Anypoint Runtime Fabric with two non-clustered replicas, reserved vCPU 1.0 and vCPU Limit 2.0.
An API consumer has complained about slow response time, and the dashboard shows the 99 percentile is greater than 120 seconds at the time of the complaint. It also shows greater than 90% CPU usage during these time periods.
In manual tests in the QA environment, the API consumer has consistently reproduced the slow response time and high CPU usage, and there were no other API requests at this time. In a brainstorming session, the engineering team has created several proposals to reduce the response time for requests.
Which proposal should be pursued first?
Answer: B
Explanation:
Scenario Analysis:
The API implementation is experiencing high CPU usage (over 90%) during bursts of requests, which correlates with slow response times, as indicated by a 99th percentile response time greater than 120 seconds.
The API implementation is running on Anypoint Runtime Fabric with two non-clustered replicas and has a reserved vCPU of 1.0 and a vCPU limit of 2.0.
The high CPU usage during bursts suggests that the current resources may not be sufficient to handle peak loads.
Evaluating the Options:
Option A (Correct Answer): Increasing the vCPU resources for each replica would provide more processing power to handle high traffic volumes, potentially reducing the response time during spikes. Since the CPU usage is consistently high during bursts, this option directly addresses the resource bottleneck.
Option B: Modifying the API client to split requests may reduce individual request load but could be complex to implement on the client side and may not fully address the high CPU issue.
Option C: Increasing the number of replicas could help distribute the load; however, with a high CPU load on each replica, adding more replicas without increasing CPU resources may not fully resolve the problem.
Option D: Throttling the client would reduce the number of requests, but this may not be acceptable if the client needs to maintain a high request rate. It also does not directly address the CPU limitations of the API implementation.
Conclusion:
Option A is the best choice as it addresses the root cause of high CPU usage by increasing the vCPU allocation, allowing the API to handle more requests efficiently. This should be pursued first before considering other options.
Refer to MuleSoft's documentation on Runtime Fabric and vCPU resource allocation for more details on optimizing API performance in high-demand environments.
NEW QUESTION # 34
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