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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Applying Integration Patterns | 11% | - Event-driven and synchronous integration - Common integration patterns and use cases - Scalability and performance patterns - Error handling and reliability patterns |
| Topic 2: Managing APIs | 12% | - Versioning and deprecation strategies - Rate limiting and throttling - API lifecycle management - API policies and security enforcement |
| Topic 3: Architecting and Deploying API Implementations | 11% | - Networking and security configuration - Runtime architecture and deployment options - CI/CD and DevOps integration - High availability and fault tolerance |
| Topic 4: Establishing Organizational and Platform Foundations | 17% | - Platform strategy and roadmap definition - Governance and organizational structure - Center for Enablement (C4E) operating model - Anypoint Platform architecture and components |
| Topic 5: Monitoring and Analyzing Application Networks | 8% | - Logging and alerting configuration - Operational visibility and optimization - Analytics and insight generation - Monitoring strategies and tools |
| Topic 6: Designing and Sharing APIs | 11% | - API design standards and best practices - API layering: Experience, Process, System APIs - API specification and documentation - Asset sharing and reuse via Anypoint Exchange |
| Topic 7: Deploying API Implementations to CloudHub | 11% | - VPC and private space configuration - Deployment optimization and scaling - CloudHub architecture and capabilities - Worker sizing and resource planning |
| Topic 8: Explaining Application Network Basics | 11% | - API-led connectivity principles - Core concepts of application networks - Benefits of modern API design |
| Topic 9: Meeting API Quality Goals | 8% | - Security and compliance standards - Performance and latency requirements - Maintainability and testability - Reliability and availability targets |
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NEW QUESTION # 55
An IT Security Compliance Auditor is assessing which nonfunctional requirements (NFRs) are already being implemented to meet security measures.
* The Web API has Rate-Limiting SLA
* Basic Authentication - LDAP
* JSON Threat Protection
* TP Allowlist policies applied
Which two NFRs-are enforced?
Answer: B,C
Explanation:
Understanding Nonfunctional Requirements (NFRs):
The NFRs in this context are related to security measures implemented for the Web API, such as rate limiting, LDAP-based authentication, JSON threat protection, and IP allowlist policies.
Evaluating the Options:
Option A (Correct Answer): The IP allowlist policy restricts access to known subnets, ensuring that API invocations come from a defined range of IPs.
Option B (Correct Answer): Basic Authentication with LDAP enforces a username/password validation, satisfying an NFR for identity verification.
Option C: Masking sensitive data is not part of the listed NFRs, as none of the mentioned policies address data masking.
Option D: XML threat protection is not mentioned, so this option is incorrect.
Option E: While rate-limiting implies performance control, it does not directly enforce a specific performance expectation.
Conclusion:
Options A and B are correct as they directly address the implemented security measures related to IP range restrictions and username/password authentication.
Refer to MuleSoft's documentation on API security policies for details on LDAP, rate limiting, and allowlist policies.
NEW QUESTION # 56
An online store's marketing team has noticed an increase in customers leaving online baskets without checking out. They suspect a technology issue is at the root cause of the baskets being left behind. They approach the Center for Enablement to ask for help identifying the issue. Multiple APIs from across all the layers of their application network are involved in the shopping application.
Which feature of the Anypoint Platform can be used to view metrics from all involved APIs at the same time?
Answer: D
Explanation:
Understanding the Need for Cross-API Monitoring:
The Center for Enablement (C4E) needs to investigate potential technical issues across multiple APIs in the application network that may be causing customers to abandon their carts.
This requires a solution that allows viewing metrics across several APIs in real-time to identify any performance issues or bottlenecks.
Evaluating Anypoint Platform Features:
Built-in Dashboards: Anypoint Platform provides built-in dashboards in Anypoint Monitoring, allowing teams to view metrics from multiple APIs in a single interface. This feature is designed to monitor API performance, latency, errors, and throughput, and is ideal for tracking performance across all layers of the application network.
Custom Dashboards: While custom dashboards allow for more tailored views, the built-in dashboards already aggregate metrics for multiple APIs, making it unnecessary to build a custom solution for this scenario.
Functional Monitoring: This feature is used to set up tests to monitor specific API functionality and uptime but is not suited for tracking metrics across multiple APIs in real-time.
API Manager: API Manager primarily focuses on managing API policies, contracts, and access control rather than providing detailed, real-time metrics across the entire application network.
Conclusion:
Option B (Built-in dashboards) is the best choice because it provides a comprehensive view of metrics from all APIs involved, enabling the C4E team to quickly identify any issues that may be contributing to abandoned shopping carts.
Refer to MuleSoft's documentation on Anypoint Monitoring and built-in dashboards for more details on configuring and using these dashboards effectively.
NEW QUESTION # 57
A team is planning to enhance an Experience API specification, and they are following API-led connectivity design principles.
What is their motivation for enhancing the API?
Answer: C
Explanation:
In API-led design, an Experience API is enhanced to improve how data is delivered to end-user applications. One primary reason to enhance an Experience API is when new data standards, such as a Canonical Data Model, are adopted. Here's why:
Canonical Data Model (CDM):
Adopting a CDM standardizes data representations across the organization, making APIs more consistent and easier to consume across various services and applications.
Updating the Experience API ensures that it delivers data in this standardized format, improving interoperability and reusability.
of Correct Answer (D):
A CDM impacts the structure and types of data the API provides, and this update would be directly relevant to an Experience API, as it is the primary point of interaction for applications.
of Incorrect Options:
Option A involves adapting to consumer-specific standards, which is against API-led design principles.
Option B involves changes in System APIs, which don't directly mandate changes to the Experience API unless data formatting adjustments are required.
Option C (IP Allowlist) relates to security rather than API design and would not motivate a functional enhancement of the API.
Reference
For more details on the use of Canonical Data Models in API-led architecture, refer to MuleSoft's guidelines on data standardization and Experience API best practices.
NEW QUESTION # 58
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?
Answer: A
Explanation:
Correct Answe r: 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
*****************************************
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 # 59
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?
Answer: D
Explanation:
Correct Answe r: 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.
NEW QUESTION # 60
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