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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Explaining Application Network Basics | 11% | - Benefits of modern API design - API-led connectivity principles - Core concepts of application networks |
| Topic 2: Meeting API Quality Goals | 8% | - Maintainability and testability - Security and compliance standards - Performance and latency requirements - Reliability and availability targets |
| Topic 3: Managing APIs | 12% | - API policies and security enforcement - Versioning and deprecation strategies - API lifecycle management - Rate limiting and throttling |
| Topic 4: Monitoring and Analyzing Application Networks | 8% | - Operational visibility and optimization - Analytics and insight generation - Monitoring strategies and tools - Logging and alerting configuration |
| Topic 5: Designing and Sharing APIs | 11% | - Asset sharing and reuse via Anypoint Exchange - API design standards and best practices - API specification and documentation - API layering: Experience, Process, System APIs |
| Topic 6: Deploying API Implementations to CloudHub | 11% | - CloudHub architecture and capabilities - VPC and private space configuration - Deployment optimization and scaling - Worker sizing and resource planning |
| Topic 7: Applying Integration Patterns | 11% | - Common integration patterns and use cases - Scalability and performance patterns - Event-driven and synchronous integration - Error handling and reliability patterns |
| Topic 8: Architecting and Deploying API Implementations | 11% | - CI/CD and DevOps integration - Networking and security configuration - Runtime architecture and deployment options - High availability and fault tolerance |
| Topic 9: Establishing Organizational and Platform Foundations | 17% | - Anypoint Platform architecture and components - Governance and organizational structure - Platform strategy and roadmap definition - Center for Enablement (C4E) operating model |
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NEW QUESTION # 51
An operations team is analyzing the effort needed to set up monitoring of their application network. They are looking at which API invocation metrics can be used to identify and predict trouble without having to write custom scripts or install additional analytics software or tools.
Which type of metrics can satisfy this goal of directly identifying and predicting failures?
Answer: D
Explanation:
To monitor an application network and predict issues without custom scripts, policy violation metrics are critical. They provide insights into potential problems by tracking instances where API usage does not conform to defined policies. Here's why this approach is suitable:
Predictive Monitoring:
Tracking API policy violations (such as rate limits or spike controls being hit) can indicate surges in traffic or misuse, which may lead to throttling or service degradation if not addressed.
By monitoring these violations, teams can proactively adjust limits or optimize API handling to prevent actual failures.
No Custom Scripting Needed:
Policy violation metrics are available within MuleSoft's Anypoint Monitoring, meaning there's no need to implement custom solutions or external tools to gather and interpret this data.
of Incorrect Options:
Option B (effectiveness based on reuse) does not directly predict failures.
Option C (past invocation counts) offers historical usage data but does not inherently identify issues.
Option D (ROI from API invocation) is a business metric and does not provide technical insights for failure prediction.
Reference
For more details on leveraging policy violation metrics for proactive monitoring, refer to MuleSoft documentation on Anypoint Monitoring.
NEW QUESTION # 52
A Mule application exposes an HTTPS endpoint and is deployed to three CloudHub workers that do not use static IP addresses. The Mule application expects a high volume of client requests in short time periods. What is the most cost-effective infrastructure component that should be used to serve the high volume of client requests?
Answer: A
Explanation:
Correct Answe r: The CloudHub shared load balancer
*****************************************
The scenario in this question can be split as below:
>> There are 3 CloudHub workers (So, there are already good number of workers to handle high volume of requests)
>> The workers are not using static IP addresses (So, one CANNOT use customer load-balancing solutions without static IPs)
>> Looking for most cost-effective component to load balance the client requests among the workers.
Based on the above details given in the scenario:
>> Runtime autoscaling is NOT at all cost-effective as it incurs extra cost. Most over, there are already 3 workers running which is a good number.
>> We cannot go for a customer-hosted load balancer as it is also NOT most cost-effective (needs custom load balancer to maintain and licensing) and same time the Mule App is not having Static IP Addresses which limits from going with custom load balancing.
>> An API Proxy is irrelevant there as it has no role to play w.r.t handling high volumes or load balancing.
So, the only right option to go with and fits the purpose of scenario being most cost-effective is - using a CloudHub Shared Load Balancer.
NEW QUESTION # 53
Which component monitors APIs and endpoints at scheduled intervals, receives reports about whether tests pass or fail, and displays statistics about API and endpoint performance?
Answer: D
Explanation:
Understanding API Functional Monitoring:
API Functional Monitoring is a feature within MuleSoft's Anypoint Platform that enables users to monitor the health and performance of APIs and endpoints by running functional tests at scheduled intervals.
It checks whether APIs are functioning as expected by running test calls and then evaluating if the response meets the desired conditions. This is particularly useful for testing endpoint availability, checking for specific data in responses, and measuring API performance over time.
Component Features:
Scheduled Intervals: Functional monitoring allows configuring tests to run at regular intervals, such as every minute, hour, or day, depending on the monitoring requirements.
Reports on Test Pass/Fail Status: After each test run, API Functional Monitoring reports whether the API passed or failed the test conditions.
Performance Statistics: It displays metrics like average response time, success rate, and error rates, giving insights into API health and performance.
Evaluating the Options:
Option A (API Analytics): API Analytics provides insights on API usage and metrics but does not involve scheduled tests for pass/fail status or endpoint health checks.
Option B (Anypoint Monitoring Dashboards): These dashboards display API metrics but do not actively test API endpoints or provide pass/fail reporting on a scheduled basis.
Option C (Correct Answer): API Functional Monitoring fits the description, as it is designed to monitor API and endpoint health with scheduled test runs and display statistics about performance.
Option D (Anypoint Runtime Manager Alerts): Runtime Manager alerts notify users of issues with application status but do not actively test endpoints at scheduled intervals.
Conclusion:
Option C (API Functional Monitoring) is the correct answer because it provides the necessary tools to test API functionality, monitor endpoint health, and display performance statistics in real-time.
Refer to MuleSoft documentation on API Functional Monitoring for further guidance on setting up and configuring these tests in Anypoint Platform.
NEW QUESTION # 54
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?
Answer: D
Explanation:
Default Alert Capabilities in Anypoint Platform:
Anypoint Platform provides out-of-the-box alerting capabilities for monitoring API invocation conditions, including setting thresholds for the number of invocations.
Alerts can be configured for conditions such as high or low traffic (invocations exceeding or falling below a defined threshold).
Evaluating the Options:
Option A: Anypoint Platform does not provide direct alerting based on DNS records.
Option B: Anypoint Platform does not provide default alerts based on whether invocations use TLS/SSL; this would require custom configuration.
Option C: Geolocation-based alerting is not natively supported in Anypoint Platform.
Option D (Correct Answer): Alerts based on API invocation thresholds (e.g., invocations falling below a set threshold) are supported and can be configured as part of the default Anypoint alerting functionality.
Conclusion:
Option D is correct, as Anypoint Platform allows configuring alerts based on the number of API invocations falling below or exceeding a threshold.
Refer to MuleSoft's documentation on Anypoint Monitoring and alert configurations for more details.
NEW QUESTION # 55
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: C
Explanation:
Correct Answe r: 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 # 56
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