100% Pass Quiz MuleSoft-Platform-Architect-I - Useful Exam Salesforce Certified MuleSoft Platform Architect I Papers

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Salesforce MuleSoft-Platform-Architect-I Exam Syllabus Topics:

Topic	Details
Topic 1	Meeting API Quality Goals: This topic focuses on designing resilience strategies, selecting appropriate caching and OS usage scenarios, and describing horizontal scaling benefits.
Topic 2	Explaining Application Network Basics: This topic includes sub-topics related to identifying and differentiating between technologies for API-led connectivity, describing the role and characteristics of web APIs, assigning APIs to tiers, and understanding Anypoint Platform components.
Topic 3	Designing and Sharing APIs: Identifying dependencies between API components, creating and publishing reusable API assets, mapping API data models between Bounded Contexts, and recognizing idempotent HTTP methods.
Topic 4	Governing Web APIs on Anypoint Platform: This topic includes sub-topics related to managing API instances and environments, selecting API policies, enforcing API policies, securing APIs, and understanding OAuth 2.0 relationships.
Topic 5	Architecting and Deploying API Implementations: It covers important aspects like using auto-discovery, identifying VPC requirements, comparing hosting options and understanding testing methods. The topic also involves automated building, testing, and deploying in a DevOps setting.
Topic 6	Monitoring and Analyzing Application Networks: It discusses Anypoint Platform components for data generation, collected metrics, and key alerts. This topic also includes specifying alerts to define Mule applications.
Topic 7	Deploying API Implementations to CloudHub: Understanding Object Store usage, selecting worker sizes, predicting app reliability and performance, and comparing load balancers. Avoiding single points of failure in deployments is also its sub-topic.
Topic 8	Designing APIs Using System, Process, and Experience Layers: Identifying suitable APIs for business processes, assigning them according to functional focus, and recommending data model approaches are its sub-topics.

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Salesforce Certified MuleSoft Platform Architect I Sample Questions (Q125-Q130):

NEW QUESTION # 125
How can the application of a rate limiting API policy be accurately reflected in the RAML definition of an API?

A. By refining the request definitions by adding a remaining Requests query parameter with description, type, and example
B. By refining the response definitions by adding the x-ratelimit-* response headers with description, type, and example
C. By refining the resource definitions by adding a description of the rate limiting policy behavior
D. By refining the response definitions by adding the out-of-the-box Anypoint Platform rate-limit-enforcement securityScheme with description, type, and example

Answer: B

Explanation:
Correct Answer : By refining the response definitions by adding the x-ratelimit-* response headers with description, type, and example
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Reference:
https://docs.mulesoft.com/api-manager/2.x/rate-limiting-and-throttling#response-headers
https://docs.mulesoft.com/api-manager/2.x/rate-limiting-and-throttling-sla-based-policies#response-headers

NEW QUESTION # 126
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?

A. The number and types of past API invocations across the application network
B. The ROI from each APT invocation
C. The effectiveness of the application network based on the level of reuse
D. The number and types of API policy violations per day

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 # 127
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?

A. In parallel, invoke the system API deployed to the primary environment and the system API deployed to the DR environment; add timeout and retry logic to the process API to avoid intermittent failures; add logic to the process API to combine the results
B. 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
C. Invoke the system API deployed to the primary environment; add retry logic to the process API to handle intermittent failures by invoking the system API deployed to the DR environment
D. 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 a copy of the process API deployed to the DR environment

Answer: B

Explanation:
Correct Answer : 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
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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 # 128
A company deploys Mule applications with default configurations through Runtime Manager to customer-hosted Mule runtimes. Each Mule application is an API implementation that exposes RESTful interfaces to API clients. The Mule runtimes are managed by the MuleSoft-hosted control plane. The payload is never used by any Logger components.
When an API client sends an HTTP request to a customer-hosted Mule application, which metadata or data (payload) is pushed to the MuleSoft-hosted control plane?

A. No data
B. Only the metadata
C. Only the data
D. The data and metadata

Answer: B

Explanation:
Understanding the Data Flow Between Mule Runtimes and Control Plane:
When Mule applications are deployed on customer-hosted Mule runtimes, the MuleSoft-hosted control plane (Anypoint Platform) can monitor and manage these applications. However, due to data privacy and security, the control plane only collects specific types of information.
Typically, only metadata about the request and response (such as headers, status codes, and timestamps) is sent to the MuleSoft-hosted control plane. The actual payload data is not transmitted unless explicitly configured, ensuring that sensitive data remains within the customer's network.
Evaluating the Options:
Option A (Only the data): This is incorrect because the payload data itself is not automatically sent to the control plane in default configurations.
Option B (No data): This is incorrect as well; while the payload is not sent, metadata is still collected and sent to the control plane.
Option C (The data and metadata): This option is incorrect because data (payload) is not transmitted to the control plane by default.
Option D (Correct Answer): Only the metadata is sent to the MuleSoft-hosted control plane by default, aligning with MuleSoft's design to prioritize security and data privacy for customer-hosted runtimes.
Conclusion:
Option D is the correct answer, as by default, only metadata is sent to the MuleSoft-hosted control plane, and not the payload. This configuration is designed to protect sensitive data from being exposed outside the customer's hosted environment.
For more details, refer to MuleSoft's documentation on telemetry data collected in customer-hosted Mule runtimes and the MuleSoft control plane.

NEW QUESTION # 129
What is true about API implementations when dealing with legal regulations that require all data processing to be performed within a certain jurisdiction (such as in the USA or the EU)?

A. They must use a Jurisdiction-local external messaging system such as Active MQ rather than Anypoint MQ
B. They must ensure ALL data is encrypted both in transit and at rest
C. They must te deployed to Anypoint Platform runtime planes that are managed by Anypoint Platform control planes, with both planes in the same Jurisdiction
D. They must avoid using the Object Store as it depends on services deployed ONLY to the US East region

Answer: C

Explanation:
Correct Answer : They must be deployed to Anypoint Platform runtime planes that are managed by Anypoint Platform control planes, with both planes in the same Jurisdiction.
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>> As per legal regulations, all data processing to be performed within a certain jurisdiction. Meaning, the data in USA should reside within USA and should not go out. Same way, the data in EU should reside within EU and should not go out.
>> So, just encrypting the data in transit and at rest does not help to be compliant with the rules. We need to make sure that data does not go out too.
>> The data that we are talking here is not just about the messages that are published to Anypoint MQ. It includes the apps running, transaction states, application logs, events, metric info and any other metadata. So, just replacing Anypoint MQ with a locally hosted ActiveMQ does NOT help.
>> The data that we are talking here is not just about the key/value pairs that are stored in Object Store. It includes the messages published, apps running, transaction states, application logs, events, metric info and any other metadata. So, just avoiding using Object Store does NOT help.
>> The only option left and also the right option in the given choices is to deploy application on runtime and control planes that are both within the jurisdiction.

NEW QUESTION # 130
......

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