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NEW QUESTION # 12
An enterprise architect has requested the Salesforce integration architect to review the following (see diagram and description) and provide recommendations after carefully considering all constraints of the enterprise systems and Salesforce Platform limits.
There are multiple eligibility systems that provide this service and are hosted externally.34 However, their current response times could take up to 90 seconds to process and return.
These eligibility systems can be acc8essed through APIs orchestrated via ESB (MuleSoft).
All requests from Salesforce must traverse the customer's API Gateway layer, which imposes a constraint of timing out requests after 9 seconds.
Which recommendation should the integration architect make?
Answer: C
NEW QUESTION # 13
A developer is researching different implementations of the Streaming API (PushTopic, Change Data Capture, Generic Streaming, Platform Events) and asks for guidance. What should the architect consider when making the recommendation?
Answer: B
Explanation:
When recommending a streaming solution, the architect must evaluate how each event type handles Record-Level Security (Sharing). Change Data Capture (CDC) is unique because it ignores sharing settings for record change events. This means all records of an enabled object generate change events, regardless of whether a particular user has access to those records in the Salesforce UI.
While CDC disregards record-level sharing, it does respect Field-Level Security (FLS). Delivered events only include the fields that the subscribing user is permitted to access. This is a critical consideration for integrations: if a system requires a "Master" view of all record changes across the enterprise (such as a data warehouse sync), CDC is the appropriate tool because it ensures no data is missed due to user-specific sharing constraints.
In contrast, PushTopic Events (Option A) provide a fixed payload based on a SOQL query and do not allow a "custom" payload in the same sense as Platform Events. Platform Events (Option C) are published from Apex or external APIs, but CDC is a platform-native feature that broadcasts automatically when a database record is modified, rather than being "published from Apex" by a developer.
NEW QUESTION # 14
A Salesforce customer is planning to roll out Salesforce for all of their sales and service staff. Senior management has requested that monitoring be in place for Operations to notify any degradation in Salesforce performance. How should an Integration consultant implement monitoring?
Answer: A
Explanation:
Effective operational monitoring focuses on the end-user experience and business outcomes rather than just raw technical metrics. An Integration consultant should identify critical business processes (e.g., "Lead Conversion" or "Order Processing") and establish benchmarks to detect performance degradation.
Monitoring purely technical limits (Option A) or individual API events (Option C) provides "noise" without context. For example, if API usage is high but the system is responding quickly, there is no degradation. However, if a critical process that normally takes 2 seconds starts taking 10 seconds, that is a clear indicator of a performance issue that impacts the business.32 The consultant should use tools like Salesforce Event Monitoring or external APM (Application Performance Management) tools to track the execution time of these key transactions. By setting alerts when performance deviates from established benchmarks, Operations can be proactively notified before users begin to lose productivity or abandon the system. This holistic approach ensures that monitoring is aligned with business value and provides actionable insights for troubleshooting bottlenecks in code, automation, or integrations.
NEW QUESTION # 15
A customer imports data from an external system into Salesforce using Bulk API. These jobs have batch sizes of 2,000 and are run in parallel mode. The batches fail frequently with the error "Max CPU time exceeded". A smaller batch size will fix this error. What should be considered when using a smaller batch size?
Answer: A
Explanation:
The Bulk API is designed to process massive datasets by breaking them into smaller batches that Salesforce processes asynchronously. When a batch fails with the "Max CPU time exceeded" error, it typically indicates that the complexity of the operations triggered by the record-such as Apex triggers, Flows, or complex sharing calculations-exceeds the 10,000ms limit within a single transaction.
Reducing the batch size is the standard architectural remedy because it reduces the number of records processed in a single transaction, thereby lowering the total CPU time consumed by those records. However, the architect must consider the impact on the overall throughput and execution time.
When batch sizes are smaller, the total number of batches required to process the same dataset increases. For instance, moving from a batch size of 2,000 to 200 for a 1-million-record dataset increases the number of batches from 500 to 5,000. Each batch carries its own overhead for initialization and finalization within the Salesforce platform. Consequently, while the individual batches are more likely to succeed, the total time required to complete the entire job will increase.
The architect should also be aware of the daily limit on the total number of batches allowed (typically 15,000 in a 24-hour period). While Option C mentions API request limits, the Bulk API is governed more strictly by its own batch limits. Option B is less likely because "parallel mode" naturally manages concurrency. Thus, the primary trade-off the architect must present to the business is a gain in reliability (successful processing) at the cost of total duration (increased sync time).
NEW QUESTION # 16
Northern Trail Outfitters is planning to perform nightly batch loads into Salesforce from an external system with a custom Java application using the Bulk API. The CIO is curious about monitoring recommendations for the jobs from the technical architect. Which recommendation should help meet the requirements?
Answer: A
Explanation:
For high-volume data loads using the Bulk API, monitoring should be performed programmatically by the orchestrating client-in this case, the custom Java application. The Bulk API is asynchronous, meaning that when you submit a job, Salesforce acknowledges the request and processes it in the background.
The Java application must actively track the state of its own jobs. Using the `getBatchInfo` (or `getJobInfo` in Bulk API 2.0) method allows the application to retrieve the real-time status of each batch. The application can check for statuses such as `Queued`, `InProgress`, `Completed`, or `Failed`. Once a batch is marked as `Completed`, the application can then call `getBatchResult` to retrieve a list of successes and failures for individual records.
Option B is architecturally unsound because Bulk API operations are designed to bypass most synchronous Apex logic to ensure performance; furthermore, creating custom records for every error in a "nightly batch load" would likely hit other platform limits (like storage or CPU) and defeat the purpose of using the Bulk API. Option C is ineffective for Bulk API monitoring, as debug logs do not capture the background processing of bulk batches and would quickly hit the log size limits.
By recommending Option A, the architect ensures that the Java application maintains full control over the integration lifecycle. The application can log errors locally, implement automated retries for transient failures, and provide the CIO with accurate, high-level reporting on the success rate of the nightly loads without placing unnecessary overhead on the Salesforce platform.
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NEW QUESTION # 17
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