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 with five attributes and Case with eight attributes in SAPI, which are combined by the PAPI 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 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.
• 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

A code-centric API documentation environment should allow API consumers to investigate and execute API client source code that demonstrates invoking one or more APIs as part of representative scenarios.
What is the most effective way to provide this type of code-centric API documentation environment using Anypoint Platform?

• A. Ensure the APIs are well documented through their Anypoint Exchange entries and API Consoles and share these pages with all API consumers
• B. Create API Notebooks and include them in the relevant Anypoint Exchange entries
• C. Make relevant APIs discoverable via an Anypoint Exchange entry
• D. Enable mocking services for each of the relevant APIs and expose them via their Anypoint Exchange entry

When could the API data model of a System API reasonably mimic the data model exposed by the corresponding backend system, with minimal improvements over the backend system's data model?

• A. When the System API can be assigned to a bounded context with a corresponding data model
• B. When the corresponding backend system is expected to be replaced in the near future
• C. When there is an existing Enterprise Data Model widely used across the organization
• D. When a pragmatic approach with only limited isolation from the backend system is deemed appropriate

A Rate Limiting policy is applied to an API implementation to protect the back-end system. Recently, there have been surges in demand that cause some API client POST requests to the API implementation to be rejected with policy-related errors, causing delays and complications to the API clients.
How should the API policies that are applied to the API implementation be changed to reduce the frequency of errors returned to API clients, while still protecting the back-end system?

• A. Remove the Rate Limiting policy and add an HTTP Caching policy
• B. Keep the Rate Limiting policy and add an SLA-based Spike Control policy
• C. Keep the Rate Limiting policy and add 9 Client ID Enforcement policy
• D. Remove the Rate Limiting policy and add a Spike Control policy

What is typically NOT a function of the APIs created within the framework called API-led connectivity?

• A. They provide an additional layer of resilience on top of the underlying backend system, thereby insulating clients from extended failure of these systems.
• B. They reduce the dependency on the underlying backend systems by helping unlock data from backend systems In a reusable and consumable way.
• C. They can compose data from various sources and combine them with orchestration logic to create higher level value.
• D. They allow for innovation at the user Interface level by consuming the underlying assets without being aware of how data Is being extracted from backend systems.