MuleSoft-Platform-Architect-I New Dumps Ppt, Valid MuleSoft-Platform-Architect-I Exam Simulator

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

Topic	Details
Topic 1	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.
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	Establishing Organizational and Platform Foundations: Advising on a Center for Enablement (C4E) and identifying KPIs, describing MuleSoft Catalyst's structure, comparing Identity and Client Management options, and identifying data residency types are essential sub-topics.
Topic 4	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 5	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 6	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.

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Salesforce Certified MuleSoft Platform Architect I Sample Questions (Q69-Q74):

NEW QUESTION # 69
A retail company is using an Order API to accept new orders. The Order API uses a JMS queue to submit orders to a backend order management service. The normal load for orders is being handled using two (2) CloudHub workers, each configured with 0.2 vCore. The CPU load of each CloudHub worker normally runs well below 70%. However, several times during the year the Order API gets four times (4x) the average number of orders. This causes the CloudHub worker CPU load to exceed 90% and the order submission time to exceed 30 seconds. The cause, however, is NOT the backend order management service, which still responds fast enough to meet the response SLA for the Order API. What is the MOST resource-efficient way to configure the Mule application's CloudHub deployment to help the company cope with this performance challenge?

A. Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
B. Permanently increase the size of each of the two (2) CloudHub workers by at least four times (4x) to one (1) vCore
C. Use a vertical CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
D. Permanently increase the number of CloudHub workers by four times (4x) to eight (8) CloudHub workers

Answer: A

Explanation:
Correct Answer : Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
*****************************************
The scenario in the question is very clearly stating that the usual traffic in the year is pretty well handled by the existing worker configuration with CPU running well below 70%. The problem occurs only "sometimes" occasionally when there is spike in the number of orders coming in.
So, based on above, We neither need to permanently increase the size of each worker nor need to permanently increase the number of workers. This is unnecessary as other than those "occasional" times the resources are idle and wasted.
We have two options left now. Either to use horizontal Cloudhub autoscaling policy to automatically increase the number of workers or to use vertical Cloudhub autoscaling policy to automatically increase the vCore size of each worker.
Here, we need to take two things into consideration:
1. CPU
2. Order Submission Rate to JMS Queue
>> From CPU perspective, both the options (horizontal and vertical scaling) solves the issue. Both helps to bring down the usage below 90%.
>> However, If we go with Vertical Scaling, then from Order Submission Rate perspective, as the application is still being load balanced with two workers only, there may not be much improvement in the incoming request processing rate and order submission rate to JMS queue. The throughput would be same as before. Only CPU utilization comes down.
>> But, if we go with Horizontal Scaling, it will spawn new workers and adds extra hand to increase the throughput as more workers are being load balanced now. This way we can address both CPU and Order Submission rate.
Hence, Horizontal CloudHub Autoscaling policy is the right and best answer.

NEW QUESTION # 70
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?

A. The total process execution time is now 100 milliseconds (.1 seconds)
B. Horizontal scaling cannot be applied to an already-running application
C. The application scalability threshold is five requests per second regardless of the horizontal scaling
D. The application scalability threshold is now 10 requests per second

Answer: C

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 # 71
An eCommerce company is adding a new Product Details feature to their website, A customer will launch the product catalog page, a new Product Details link will appear by product where they can click to retrieve the product detail description. Product detail data is updated with product update releases, once or twice a year, Presently the database response time has been very slow due to high volume.
What action retrieves the product details with the lowest response time, fault tolerant, and consistent data?

A. Use an object store to store and retrieve the product details originally read from a database and return them within the API response
B. Select the product details from a database in a Cache scope and return them within the API response
C. Select the product details from a database and return them within the API response
D. Select the product details from a database and put them in Anypoint MQ; the Anypoint MO subseriber will receive the product details and return them within the API response

Answer: A

Explanation:
Scenario Analysis:
The eCommerce company's Product Details feature requires low response time and consistent data for a feature where data rarely changes (only once or twice a year).
The database response time is slow due to high volume, so querying the database directly on each request would lead to poor performance and higher response times.
Optimal Solution Requirements:
Low Response Time: Data retrieval should be fast and not depend on database performance.
Fault Tolerance and Data Consistency: Cached or stored data should be consistent and resilient in case of database unavailability, as the product details data changes infrequently.
Evaluating the Options:
Option A: Using a Cache scope would temporarily store the product details in memory, which could improve performance but might not be suitable for infrequent updates (only twice a year), as cache expiration policies typically require shorter durations.
Option B: Storing product details in Anypoint MQ and then retrieving it through a subscriber is not suitable for this use case. Anypoint MQ is better for messaging rather than as a data storage mechanism.
Option C (Correct Answer): Using an object store to store and retrieve product details is ideal. Object stores in MuleSoft are designed for persistent storage of key-value pairs, which allows storing data retrieved from the database initially. This provides quick, consistent access without querying the database on every request, aligning with requirements for low response time, fault tolerance, and data consistency.
Option D: Selecting data directly from the database for each request would not meet the performance requirement due to known slow response times from the database.
Conclusion:
Option C is the best answer, as using an object store allows caching the infrequently updated product details. This approach reduces the dependency on the database, significantly improving response time and ensuring consistent data.
Refer to MuleSoft documentation on Object Store v2 and best practices for data caching to implement this solution effectively.

NEW QUESTION # 72
A customer has an ELA contract with MuleSoft. An API deployed to CloudHub is consistently experiencing performance issues. Based on the root cause analysis, it is determined that autoscaling needs to be applied.
How can this be achieved?

A. Configure a policy based on CPU usage so that CloudHub auto-adjusts the number of workers/replicas (horizontal scaling)
B. Configure a policy so that when the number of HTTP requests reaches a certain threshold the number of workers/replicas increases (horizontal scaling)
C. Configure two separate policies: When CPU and memory reach certain threshold, increase the worker/replica type (vertical sealing) and the number of workers/replicas (horizontal sealing)
D. Configure a policy so that when the response time reaches a certain threshold the worker/replica type increases (vertical scaling)

Answer: A

Explanation:
In MuleSoft CloudHub, autoscaling is essential to managing application load efficiently. CloudHub supports horizontal scaling based on CPU usage, which is well-suited to applications experiencing variable demand and needing responsive resource allocation.
Autoscaling on CloudHub:
Horizontal scaling increases the number of workers in response to CPU usage thresholds, allowing the application to handle higher loads dynamically. This approach improves performance without downtime or manual intervention.
Why Option C is Correct:
Setting up autoscaling based on CPU usage aligns with MuleSoft's best practices for scalable and responsive applications on CloudHub, particularly in an environment with fluctuating load patterns.
Option C correctly leverages CloudHub's autoscaling features based on resource metrics, which are part of CloudHub's managed scaling solutions.
of Incorrect Options:
Option A (based on HTTP request thresholds) and Option B (separate policies for CPU and memory) do not represent CloudHub's recommended scaling practices.
Option D suggests vertical scaling based on response time, which is not how CloudHub handles autoscaling.
Reference
For more on CloudHub's autoscaling configuration, refer to MuleSoft documentation on CloudHub autoscaling policies.

NEW QUESTION # 73
A client has several applications running on the Salesforce service cloud. The business requirement for integration is to get daily data changes from Account and Case Objects. Data needs to be moved to the client's private cloud AWS DynamoDB instance as a single JSON and the business foresees only wanting five attributes from the Account object, which has 219 attributes (some custom) and eight attributes from the Case Object.
What design should be used to support the API/ Application data model?

A. Create separate entities for Account and Case Objects by mimicking all the attributes in SAPI, which are combined by the PAPI and filtered to provide JSON output containing 13 attributes.
B. Start implementing an Enterprise Data Model by defining enterprise Account and Case Objects and implement SAPI and DynamoDB tables based on the Enterprise Data Model,
C. Create separate entities for Account with five attributes and Case with eight attributes in SAPI, which are combined by the PAPI to provide JSON output containing 13 attributes.
D. Request client's AWS project team to replicate all the attributes and create Account and Case JSON table in DynamoDB. Then create separate entities for Account and Case Objects by mimicking all the attributes in SAPI to transfer ISON data to DynamoD for respective Objects

Answer: C

Explanation:
Understanding the Requirements:
The business needs to transfer daily data changes from the Salesforce Account and Case objects to AWS DynamoDB in a single JSON format.
Only a subset of attributes (5 from Account and 8 from Case) is required, so it is not necessary to include all 219 attributes of the Account object.
Design Approach:
A System API (SAPI) should be created for each Salesforce object (Account and Case), exposing only the required fields (5 attributes for Account and 8 for Case).
A Process API (PAPI) can be used to aggregate and transform the data from these SAPIs, combining the 13 selected attributes from Account and Case into a single JSON structure for DynamoDB.
Evaluating the Options:
Option A: Mimicking all attributes in the SAPI is inefficient and unnecessary, as only 13 attributes are required.
Option B: Replicating all attributes in DynamoDB is excessive and would result in higher storage and processing costs, which is unnecessary given the requirement for only a subset of attributes.
Option C: Implementing an Enterprise Data Model could be useful in broader data management but is not required here, as the focus is on a lightweight integration.
Option D (Correct Answer): Creating separate entities in SAPI for Account and Case with only the required attributes and using the PAPI to aggregate them into a single JSON is the most efficient and meets the requirements effectively.
Conclusion:
Option D is the best choice as it provides a lightweight, efficient design that meets the requirements by transferring only the necessary attributes and minimizing resource use.
Refer to MuleSoft's best practices for API-led connectivity and data modeling to structure SAPIs and PAPIs efficiently.

NEW QUESTION # 74
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MuleSoft-Platform-Architect-I New Dumps Ppt, Valid MuleSoft-Platform-Architect-I Exam Simulator

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

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Salesforce Certified MuleSoft Platform Architect I Sample Questions (Q69-Q74):

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