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質問 # 261
A manufacturing company is developing a new set of APIs for its retail business. One of the APIs is a Master Look Up API, which is a System API, The API uses a persistent object-store. This API will be used by almost all other APIs to provide master lookup data.
The Master Look Up API is deployed on two CloudHub workers of 0.1 vCore each because there is a lot of master data to be cached. Most of the master lookup data is stored as a key-value pair. The cache gets refreshed if the key is not found in the cache.
During performance testing, it was determined that the Master Look Up API has a high response time due to the latency of database queries executed to fetch the master lookup data.
What two methods can be used to resolve these performance issues?
Choose 2 answers
正解：A、B
解説：
AnTo resolve performance issues with the Master Look Up API, the following methods can be applied:
* Implement the HTTP caching policy for all GET endpoints for the Master Look Up API: This caching policy helps reduce the number of database queries by storing frequently accessed data in cache, thus improving response times for subsequent requests.
* Upgrade the vCore size from 0.1 vCore to 0.2 vCore: Increasing the vCore size can provide more computational resources, which can improve the processing power and overall performance of the API, particularly under high load conditions.
References:
* HTTP Caching Policy
* Scaling and Performance
swer: A
質問 # 262
According to MuleSoft's API development best practices, which type of API development approach starts with writing and approving an API contract?
正解：C
解説：
MuleSoft's API development best practices emphasize a design-first approach, which starts with writing and approving an API contract before any implementation begins. This approach ensures that the API's interface is agreed upon and understood by all stakeholders before the backend is built. It involves creating an API specification using tools like RAML or OpenAPI, which serves as a blueprint for development. This method promotes better planning, communication, and alignment between different teams and stakeholders, leading to more efficient and predictable API development processes.
References:
* API Design Best Practices
* MuleSoft's Approach to API Development
質問 # 263
A banking company is developing a new set of APIs for its online business. One of the critical API's is a master lookup API which is a system API. This master lookup API uses persistent object store. This API will be used by all other APIs to provide master lookup data.
Master lookup API is deployed on two cloudhub workers of 0.1 vCore each because there is a lot of master data to be cached. Master lookup data is stored as a key value pair. The cache gets refreshed if they key is not found in the cache.
Doing performance testing it was observed that the Master lookup API has a higher response time due to database queries execution to fetch the master lookup data.
Due to this performance issue, go-live of the online business is on hold which could cause potential financial loss to Bank.
As an integration architect, which of the below option you would suggest to resolve performance issue?
正解：B
解説：
A: Implement HTTP caching policy for all GET endpoints for the master lookup API and implement locking to synchronize access to object store Comprehensive Detailed Step by Step ExplanationTo resolve the performance issue observed during the performance testing of the Master lookup API, the best approach involves caching and synchronization:
* HTTP Caching Policy:
* Purpose: Reduces the load on the database by caching the responses for GET requests. Once a response is cached, subsequent requests for the same resource can be served from the cache instead of querying the database.
* Implementation: Apply the HTTP caching policy to all GET endpoints of the Master lookup API. This policy ensures that the frequently accessed master lookup data is stored in the cache and served quickly.
* Benefits: This significantly reduces the number of database queries, thus lowering the response time and improving performance.
* Object Store Locking:
* Purpose: Ensures data consistency and prevents race conditions when multiple workers attempt to access or update the cache concurrently.
* Implementation: Use a locking mechanism to synchronize access to the object store. This can be achieved by using MuleSoft's Object Store Connector with lock/unlock operations.
* Benefits: Prevents multiple database queries for the same key and ensures that only one worker updates the cache at a time, thus avoiding redundant operations and maintaining data integrity.
By implementing these two strategies, the response time of the Master lookup API will be significantly improved, and the performance issue will be resolved.
References
* MuleSoft HTTP Caching Policy
* MuleSoft Object Store Connector
質問 # 264
An insurance company has an existing API which is currently used by customers. API is deployed to customer hosted Mule runtime cluster. The load balancer that is used to access any APIs on the mule cluster is only configured to point to applications hosted on the server at port 443.
Mule application team of a company attempted to deploy a second API using port 443 but the application will not start and checking logs shows an error indicating the address is already in use.
Which steps must the organization take to resolve this error and allow customers to access both the API's?
正解：B
質問 # 265
An insurance provider is implementing Anypoint platform to manage its application infrastructure and is using the customer hosted runtime for its business due to certain financial requirements it must meet. It has built a number of synchronous API's and is currently hosting these on a mule runtime on one server These applications make use of a number of components including heavy use of object stores and VM queues.
Business has grown rapidly in the last year and the insurance provider is starting to receive reports of reliability issues from its applications.
The DevOps team indicates that the API's are currently handling too many requests and this is over loading the server. The team has also mentioned that there is a significant downtime when the server is down for maintenance.
As an integration architect, which option would you suggest to mitigate these issues?
正解：A
解説：
To address the reliability and scalability issues faced by the insurance provider, adding a load balancer and configuring additional servers in a cluster configuration is the optimal solution. Here's why:
* Load Balancing: Implementing a load balancer will help distribute incoming API requests evenly across multiple servers. This prevents any single server from becoming a bottleneck, thereby improving the overall performance and reliability of the system.
* Cluster Configuration: By setting up a cluster configuration, you ensure that multiple servers work together as a single unit. This provides several benefits:
* High Availability: If one server goes down, the load balancer can redirect traffic to other available servers in the cluster, minimizing downtime.
* Scalability: Additional servers can be added to the cluster to handle increased load as the business grows.
* Fault Tolerance: Clustering allows for automatic failover and recovery, ensuring that applications remain available even if one server fails.
* Maintenance: With a cluster configuration, servers can be taken offline for maintenance one at a time without affecting the overall availability of the applications, as the load balancer can redirect traffic to the remaining servers.
* VM Queues and Object Stores: In a clustered environment, the use of VM queues and object stores can be more efficiently managed as these resources are distributed across multiple servers, reducing the risk of contention and improving throughput.
MuleSoft Documentation on Clustering: https://docs.mulesoft.com/mule-runtime/4.3/clustering Best Practices for Scaling Mule Applications: https://blogs.mulesoft.com/dev/mule-dev/mule-4-scaling- applications/
質問 # 266
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