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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Monitoring and Analyzing Application Networks | 8% | - Analytics and insight generation - Monitoring strategies and tools - Logging and alerting configuration - Operational visibility and optimization |
| Topic 3: 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 4: Architecting and Deploying API Implementations | 11% | - CI/CD and DevOps integration - Networking and security configuration - High availability and fault tolerance - Runtime architecture and deployment options |
| Topic 5: Managing APIs | 12% | - API lifecycle management - Versioning and deprecation strategies - API policies and security enforcement - Rate limiting and throttling |
| Topic 6: Explaining Application Network Basics | 11% | - Benefits of modern API design - API-led connectivity principles - Core concepts of application networks |
| Topic 7: Meeting API Quality Goals | 8% | - Performance and latency requirements - Security and compliance standards - Maintainability and testability - Reliability and availability targets |
| Topic 8: Deploying API Implementations to CloudHub | 11% | - Deployment optimization and scaling - CloudHub architecture and capabilities - VPC and private space configuration - Worker sizing and resource planning |
| Topic 9: Applying Integration Patterns | 11% | - Error handling and reliability patterns - Common integration patterns and use cases - Scalability and performance patterns - Event-driven and synchronous integration |
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NEW QUESTION # 69
An organization has several APIs that accept JSON data over HTTP POST. The APIs are all publicly available and are associated with several mobile applications and web applications.
The organization does NOT want to use any authentication or compliance policies for these APIs, but at the same time, is worried that some bad actor could send payloads that could somehow compromise the applications or servers running the API implementations.
What out-of-the-box Anypoint Platform policy can address exposure to this threat?
Answer: D
Explanation:
Correct Answe r: Apply a JSON threat protection policy to all APIs to detect potential threat vectors
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>> Usually, if the APIs are designed and developed for specific consumers (known consumers/customers) then we would IP Whitelist the same to ensure that traffic only comes from them.
>> However, as this scenario states that the APIs are publicly available and being used by so many mobile and web applications, it is NOT possible to identify and blacklist all possible bad actors.
>> So, JSON threat protection policy is the best chance to prevent any bad JSON payloads from such bad actors.
NEW QUESTION # 70
A system API has a guaranteed SLA of 100 ms per request. The system API is deployed to a primary environment as well as to a disaster recovery (DR) environment, with different DNS names in each environment. An upstream process API invokes the system API and the main goal of this process API is to respond to client requests in the least possible time. In what order should the system APIs be invoked, and what changes should be made in order to speed up the response time for requests from the process API?
Answer: B
Explanation:
Correct Answe r: In parallel, invoke the system API deployed to the primary environment and the system API deployed to the DR environment, and ONLY use the first response.
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>> The API requirement in the given scenario is to respond in least possible time.
>> The option that is suggesting to first try the API in primary environment and then fallback to API in DR environment would result in successful response but NOT in least possible time. So, this is NOT a right choice of implementation for given requirement.
>> Another option that is suggesting to ONLY invoke API in primary environment and to add timeout and retries may also result in successful response upon retries but NOT in least possible time. So, this is also NOT a right choice of implementation for given requirement.
>> One more option that is suggesting to invoke API in primary environment and API in DR environment in parallel using Scatter-Gather would result in wrong API response as it would return merged results and moreover, Scatter-Gather does things in parallel which is true but still completes its scope only on finishing all routes inside it. So again, NOT a right choice of implementation for given requirement The Correct choice is to invoke the API in primary environment and the API in DR environment parallelly, and using ONLY the first response received from one of them.
NEW QUESTION # 71
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)
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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 # 72
A customer wants to monitor and gain insights about the number of requests coming in a given time period as well as to measure key performance indicators (response times, CPU utilization, number of active APIs).
Which tool provides these data insights?
Answer: B
Explanation:
Understanding Anypoint Monitoring and Its Capabilities:
Anypoint Monitoring provides comprehensive visibility into Mule applications, offering metrics and analytics such as request counts, response times, CPU utilization, memory usage, and other key performance indicators (KPIs). This tool is designed to help teams monitor API usage, troubleshoot issues, and optimize application performance.
Evaluating the Options:
Option A (Correct Answer): Anypoint Monitoring is the ideal tool for this requirement. It provides real-time insights into metrics such as the number of requests, response times, CPU utilization, and active API usage.
Option B (API Manager): API Manager focuses on API lifecycle management, including applying policies, managing contracts, and setting access controls. It does not provide performance monitoring or KPI tracking.
Option C (Runtime Alerts): Runtime Alerts can notify users of specific conditions, like high CPU usage, but they do not provide a full suite of metrics or insights over a given time period.
Option D (Functional Monitoring): Functional Monitoring focuses on functional testing of APIs rather than performance and usage metrics. It does not provide continuous KPI tracking.
Conclusion:
Option A is the correct answer. Anypoint Monitoring is the most suitable tool to track the specified metrics, providing detailed insights into API requests, response times, CPU usage, and active API counts.
For further details, refer to MuleSoft's Anypoint Monitoring documentation on configuring dashboards and tracking performance metrics.
NEW QUESTION # 73
A company has started to create an application network and is now planning to implement a Center for Enablement (C4E) organizational model. What key factor would lead the company to decide upon a federated rather than a centralized C4E?
Answer: B
Explanation:
Correct Answe r: When development is already organized into several independent initiatives or groups
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>> It would require lot of process effort in an organization to have a single C4E team coordinating with multiple already organized development teams which are into several independent initiatives. A single C4E works well with different teams having at least a common initiative. So, in this scenario, federated C4E works well instead of centralized C4E.
NEW QUESTION # 74
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