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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Architecting and Deploying API Implementations | 11% | - High availability and fault tolerance - CI/CD and DevOps integration - Networking and security configuration - Runtime architecture and deployment options |
| Meeting API Quality Goals | 8% | - Security and compliance standards - Performance and latency requirements - Maintainability and testability - Reliability and availability targets |
| Designing and Sharing APIs | 11% | - API layering: Experience, Process, System APIs - API specification and documentation - Asset sharing and reuse via Anypoint Exchange - API design standards and best practices |
| Managing APIs | 12% | - Rate limiting and throttling - API policies and security enforcement - Versioning and deprecation strategies - API lifecycle management |
| Monitoring and Analyzing Application Networks | 8% | - Analytics and insight generation - Logging and alerting configuration - Operational visibility and optimization - Monitoring strategies and tools |
| Applying Integration Patterns | 11% | - Scalability and performance patterns - Common integration patterns and use cases - Error handling and reliability patterns - Event-driven and synchronous integration |
| Deploying API Implementations to CloudHub | 11% | - Deployment optimization and scaling - Worker sizing and resource planning - VPC and private space configuration - CloudHub architecture and capabilities |
| Explaining Application Network Basics | 11% | - Core concepts of application networks - API-led connectivity principles - Benefits of modern API design |
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NEW QUESTION # 85
An Order API triggers a sequence of other API calls to look up details of an order's items in a back-end inventory database. The Order API calls the OrderItems process API, which calls the Inventory system API. The Inventory system API performs database operations in the back-end inventory database.
The network connection between the Inventory system API and the database is known to be unreliable and hang at unpredictable times.
Where should a two-second timeout be configured in the API processing sequence so that the Order API never waits more than two seconds for a response from the Orderltems process API?
Answer: C
Explanation:
Understanding the API Flow and Timeout Requirement:
The Order API initiates a call to the OrderItems process API, which in turn calls the Inventory system API to fetch details from the inventory database.
The requirement specifies that the Order API should not wait more than two seconds for a response from the OrderItems process API, even if there are delays further down the chain (between Inventory system API and the database).
Choosing the Appropriate Timeout Location:
Setting the timeout at the OrderItems process API level ensures that if the Inventory system API takes longer than two seconds to respond, the OrderItems process API will terminate the request and send a timeout response back to the Order API. This prevents the Order API from waiting indefinitely due to the unreliable connection to the database.
If the timeout were set in the Inventory system API or database, it would not help the Order API directly, as the OrderItems process API would still be waiting for a response.
Detailed Analysis of Each Option:
Option A (Correct Answer): Setting the timeout in the OrderItems process API allows it to control how long it waits for a response from the Inventory system API. If the Inventory system API does not respond within two seconds, the OrderItems process API can terminate the call and return a timeout response to the Order API, meeting the requirement.
Option B: Setting the timeout in the Order API would not limit the wait time at the OrderItems process API level, meaning the OrderItems process API could still wait indefinitely for the Inventory system API, leading to a longer delay.
Option C: Setting the timeout in the Inventory system API only affects the connection to the database and does not influence how long the OrderItems process API waits for the Inventory system API's response.
Option D: Setting a timeout in the database is not feasible in this context since database timeouts are typically configured for database operations and would not directly control the API response times in the overall API chain.
Conclusion:
Option A is the best choice, as it ensures that the OrderItems process API does not hold the Order API longer than the required two seconds, even if the downstream connection to the database hangs. This configuration aligns with MuleSoft best practices for setting timeouts in API orchestration to manage dependencies and prevent delays across a chain of API calls.
For additional information on timeout settings, refer to MuleSoft documentation on handling timeouts and API orchestration best practices.
NEW QUESTION # 86
When designing an upstream API and its implementation, the development team has been advised to NOT set timeouts when invoking a downstream API, because that downstream API has no SLA that can be relied upon. This is the only downstream API dependency of that upstream API.
Assume the downstream API runs uninterrupted without crashing. What is the impact of this advice?
Answer: D
Explanation:
Correct Answe r: An SLA for the upstream API CANNOT be provided.
*****************************************
>> First thing first, the default HTTP response timeout for HTTP connector is 10000 ms (10 seconds). NOT 500 ms.
>> Mule runtime does NOT apply any such "load-dependent" timeouts. There is no such behavior currently in Mule.
>> As there is default 10000 ms time out for HTTP connector, we CANNOT always guarantee that the invocation of the downstream API will run to completion without timing out due to its unreliable SLA times. If the response time crosses 10 seconds then the request may time out.
The main impact due to this is that a proper SLA for the upstream API CANNOT be provided.
NEW QUESTION # 87
What Mule application deployment scenario requires using Anypoint Platform Private Cloud Edition or Anypoint Platform for Pivotal Cloud Foundry?
Answer: A
Explanation:
Correct Answe r: When regulatory requirements mandate on-premises processing of EVERY data item, including meta-data.
*****************************************
We need NOT require to use Anypoint Platform PCE or PCF for the below. So these options are OUT.
>> We can make ALL applications highly available across multiple data centers using CloudHub too.
>> We can use Anypoint VPN and tunneling from CloudHub to connect to ALL backend systems in the application network that are deployed in the organization's intranet.
>> We can use Anypoint VPC and Firewall Rules to make ALL APIs private and NOT exposed to the public cloud.
Only valid reason in the given options that requires to use Anypoint Platform PCE/ PCF is - When regulatory requirements mandate on-premises processing of EVERY data item, including meta-data.
NEW QUESTION # 88
Which layer in the API-led connectivity focuses on unlocking key systems, legacy systems, data sources etc and exposes the functionality?
Answer: B
Explanation:
Correct Answe r: System Layer
The APIs used in an API-led approach to connectivity fall into three categories:
System APIs - these usually access the core systems of record and provide a means of insulating the user from the complexity or any changes to the underlying systems. Once built, many users, can access data without any need to learn the underlying systems and can reuse these APIs in multiple projects.
Process APIs - These APIs interact with and shape data within a single system or across systems (breaking down data silos) and are created here without a dependence on the source systems from which that data originates, as well as the target channels through which that data is delivered.
Experience APIs - Experience APIs are the means by which data can be reconfigured so that it is most easily consumed by its intended audience, all from a common data source, rather than setting up separate point-to-point integrations for each channel. An Experience API is usually created with API-first design principles where the API is designed for the specific user experience in mind.
NEW QUESTION # 89
An application updates an inventory running only one process at any given time to keep the inventory consistent. This process takes 200 milliseconds (.2 seconds) to execute; therefore, the scalability threshold of the application is five requests per second.
What is the impact on the application if horizontal scaling is applied, thereby increasing the number of Mule workers?
Answer: A
Explanation:
Given that the application is designed to handle only one process at a time to maintain data consistency, here's why horizontal scaling won't increase the processing limit:
Single-Process Constraint:
The application limits to processing one transaction at a time due to its design for consistency, meaning horizontal scaling (adding more workers) does not increase processing speed beyond this limit.
Execution Time:
Since each request takes 200 ms, five requests per second is the maximum processing threshold. Increasing the number of workers does not bypass this single-process limitation.
of Correct Answer (A):
The scalability remains at five requests per second, as this constraint is intrinsic to the application's design.
of Incorrect Options:
Option B suggests a change in execution time, which horizontal scaling does not affect.
Option C assumes doubling the throughput, which isn't possible due to the single-threaded nature of the application.
Option D suggests horizontal scaling cannot apply, which is incorrect; however, scaling does not increase throughput in this context.
Reference
For more on understanding scaling and concurrency in Mule applications, see MuleSoft's documentation on application performance and scaling limitations.
NEW QUESTION # 90
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