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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing APIs | 12% | - API lifecycle management - API policies and security enforcement - Versioning and deprecation strategies - Rate limiting and throttling |
| Topic 2: Monitoring and Analyzing Application Networks | 8% | - Logging and alerting configuration - Monitoring strategies and tools - Analytics and insight generation - Operational visibility and optimization |
| Topic 3: Applying Integration Patterns | 11% | - Event-driven and synchronous integration - Common integration patterns and use cases - Error handling and reliability patterns - Scalability and performance patterns |
| Topic 4: Meeting API Quality Goals | 8% | - Performance and latency requirements - Security and compliance standards - Reliability and availability targets - Maintainability and testability |
| Topic 5: Establishing Organizational and Platform Foundations | 17% | - Governance and organizational structure - Platform strategy and roadmap definition - Anypoint Platform architecture and components - Center for Enablement (C4E) operating model |
| Topic 6: Explaining Application Network Basics | 11% | - Benefits of modern API design - Core concepts of application networks - API-led connectivity principles |
| Topic 7: Deploying API Implementations to CloudHub | 11% | - VPC and private space configuration - Worker sizing and resource planning - Deployment optimization and scaling - CloudHub architecture and capabilities |
| Topic 8: Architecting and Deploying API Implementations | 11% | - Networking and security configuration - High availability and fault tolerance - Runtime architecture and deployment options - CI/CD and DevOps integration |
| Topic 9: Designing and Sharing APIs | 11% | - API design standards and best practices - API specification and documentation - API layering: Experience, Process, System APIs - Asset sharing and reuse via Anypoint Exchange |
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NEW QUESTION # 10
To minimize operation costs, a customer wants to use a CloudHub 1.0 solution. The customer's requirements are:
* Separate resources with two Business groups
* High-availability (HA) for all APIs
* Route traffic via Dedicated load balancer (DLBs)
* Separate environments into production and non-production
Which solution meets the customer's needs?
Answer: A
Explanation:
Understanding the Requirements:
Business Groups: The solution must support two business groups, which typically require separate VPCs for logical separation.
High Availability (HA): Requires deploying resources across multiple availability zones.
Dedicated Load Balancer (DLB): Traffic should be routed via DLBs, which operate within VPCs on CloudHub.
Separate Environments: There needs to be separation between production and non-production environments.
Evaluating the Options:
Option A: Using a single production and non-production VPC and differentiating business groups via availability zones is not ideal as it does not provide full separation for each business group, and using maximum CIDR allocation is wasteful.
Option B (Correct Answer): Creating separate production and non-production VPCs per business group with minimized CIDR blocks, multiple availability zones, and multiple workers per application for HA meets all requirements effectively.
Option C: While this option separates VPCs per business group, it does not fully address the requirement for HA across availability zones by specifying multi-zone deployment only during API deployment, which may not guarantee redundancy.
Option D: Configuring subnets to differentiate business groups within a single production and non-production VPC does not fully separate the business groups, which is a requirement.
Conclusion:
Option B is the best choice as it meets the requirements for high availability, business group separation, and cost efficiency by using minimized CIDR allocations and deploying multiple workers across availability zones.
For further reference, refer to MuleSoft's documentation on VPC configuration and high availability deployment strategies.
NEW QUESTION # 11
Question 10: Skipped
An API implementation returns three X-RateLimit-* HTTP response headers to a requesting API client. What type of information do these response headers indicate to the API client?
Answer: C
Explanation:
Correct Answe r: The remaining capacity allowed by the API implementation.
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>> Reference: https://docs.mulesoft.com/api-manager/2.x/rate-limiting-and-throttling-sla-based-policies#response-headers
NEW QUESTION # 12
An API implementation is updated. When must the RAML definition of the API also be updated?
Answer: C
Explanation:
Correct Answe r: When the API implementation changes the structure of the request or response messages
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>> RAML definition usually needs to be touched only when there are changes in the request/response schemas or in any traits on API.
>> It need not be modified for any internal changes in API implementation like performance tuning, backend system migrations etc..
NEW QUESTION # 13
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,D
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 # 14
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: B
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 # 15
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