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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Meeting API Quality Goals | 8% | - Unit & Integration Testing - Resilience & Reliability Patterns - API Performance Optimization |
| Topic 2: Managing APIs | 12% | - API Lifecycle Management - API Versioning - API Security (Policies, SLAs) |
| Topic 3: Establishing Organizational and Platform Foundations | 17% | - Governance and Compliance Framework - Platform Foundation Setup - IT Delivery & C4E Operating Model |
| Topic 4: Deploying API Implementations to CloudHub | 11% | - CloudHub Architecture - CloudHub 2.0 & Runtime Fabric - Deployment Strategies |
| Topic 5: Monitoring and Analyzing Application Networks | 8% | - Analytics & Business Insights - Alerting & Auditing - Anypoint Monitoring Tools |
| Topic 6: Architecting and Deploying API Implementations | 11% | - Disaster Recovery (DR) Strategies - Mule Runtime Architecture - High Availability (HA) Design |
| Topic 7: Designing and Sharing APIs | 11% | - API Design Best Practices - Anypoint Exchange for Sharing Assets - API-Led Connectivity Approach |
| Topic 8: Applying Integration Patterns | 11% | - Batch Processing - Integration Scenarios & Patterns - Event-Driven Architecture (EDA) |
| Topic 9: Understanding the MuleSoft Platform & Application Networks | 11% | - C4E (Center for Enablement) Definition - MuleSoft Anypoint Platform Core Concepts - Application Network Fundamentals |
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NEW QUESTION # 118
The implementation of a Process API must change.
What is a valid approach that minimizes the impact of this change on API clients?
Answer: D
Explanation:
Correct Answe r: Implement required changes to the Process API implementation so that, whenever possible, the Process API's RAML definition remains unchanged.
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Key requirement in the question is:
>> Approach that minimizes the impact of this change on API clients
Based on above:
>> Updating the RAML definition would possibly impact the API clients if the changes require any thing mandatory from client side. So, one should try to avoid doing that until really necessary.
>> Implementing the changes as a completely different API and then redirectly the clients with 3xx status code is really upsetting design and heavily impacts the API clients.
>> Organisations and IT cannot simply postpone the changes required until all API consumers acknowledge they are ready to migrate to a new Process API or API version. This is unrealistic and not possible.
The best way to handle the changes always is to implement required changes to the API implementations so that, whenever possible, the API's RAML definition remains unchanged.
NEW QUESTION # 119
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 # 120
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: A
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 # 121
What Anypoint Platform Capabilities listed below fall under APIs and API Invocations/Consumers category? Select TWO.
Answer: A
Explanation:
Correct Answers: API Design and Development and API Runtime Execution and Hosting
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>> API Design and Development - Anypoint Studio, Anypoint Design Center, Anypoint Connectors
>> API Runtime Execution and Hosting - Mule Runtimes, CloudHub, Runtime Services
>> API Operations and Management - Anypoint API Manager, Anypoint Exchange
Correct Answers: API Operations and Management and API Consumer Engagement
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>> API Design and Development - Anypoint Studio, Anypoint Design Center, Anypoint Connectors
>> API Runtime Execution and Hosting - Mule Runtimes, CloudHub, Runtime Services
>> API Operations and Management - Anypoint API Manager, Anypoint Exchange
>> API Consumer Management - API Contracts, Public Portals, Anypoint Exchange, API Notebooks
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NEW QUESTION # 122
When could the API data model of a System API reasonably mimic the data model exposed by the corresponding backend system, with minimal improvements over the backend system's data model?
Answer: A
Explanation:
Correct Answe r: When a pragmatic approach with only limited isolation from the backend system is deemed appropriate.
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General guidance w.r.t choosing Data Models:
>> If an Enterprise Data Model is in use then the API data model of System APIs should make use of data types from that Enterprise Data Model and the corresponding API implementation should translate between these data types from the Enterprise Data Model and the native data model of the backend system.
>> If no Enterprise Data Model is in use then each System API should be assigned to a Bounded Context, the API data model of System APIs should make use of data types from the corresponding Bounded Context Data Model and the corresponding API implementation should translate between these data types from the Bounded Context Data Model and the native data model of the backend system. In this scenario, the data types in the Bounded Context Data Model are defined purely in terms of their business characteristics and are typically not related to the native data model of the backend system. In other words, the translation effort may be significant.
>> If no Enterprise Data Model is in use, and the definition of a clean Bounded Context Data Model is considered too much effort, then the API data model of System APIs should make use of data types that approximately mirror those from the backend system, same semantics and naming as backend system, lightly sanitized, expose all fields needed for the given System API's functionality, but not significantly more and making good use of REST conventions.
The latter approach, i.e., exposing in System APIs an API data model that basically mirrors that of the backend system, does not provide satisfactory isolation from backend systems through the System API tier on its own. In particular, it will typically not be possible to "swap out" a backend system without significantly changing all System APIs in front of that backend system and therefore the API implementations of all Process APIs that depend on those System APIs! This is so because it is not desirable to prolong the life of a previous backend system's data model in the form of the API data model of System APIs that now front a new backend system. The API data models of System APIs following this approach must therefore change when the backend system is replaced.
On the other hand:
>> It is a very pragmatic approach that adds comparatively little overhead over accessing the backend system directly
>> Isolates API clients from intricacies of the backend system outside the data model (protocol, authentication, connection pooling, network address, ...)
>> Allows the usual API policies to be applied to System APIs
>> Makes the API data model for interacting with the backend system explicit and visible, by exposing it in the RAML definitions of the System APIs
>> Further isolation from the backend system data model does occur in the API implementations of the Process API tier
NEW QUESTION # 123
......
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