Salesforce Data-Architect Dumps Questions Answers

According to the Salesforce documentation1, the query plan tool is a tool that can be enabled and used in the developer console to analyze the performance of a SOQL query. The query plan tool shows the cost, cardinality, sObject type, and relative cost of each query plan that Salesforce considers for a query. The relative cost indicates how expensive a query plan is compared to the Force.com query optimizer threshold. A query plan with a relative cost above 1.0 is likely to cause a time-out error.

To identify why the query is taking too long, a data architect should use the query plan tool in the developer console (option B). This way, the data architect can see which query plan is chosen by Salesforce and how it affects the performance of the query. The data architect can also use the query plan tool to optimize the query by adding indexes, filters, or limits to reduce the cost and improve the efficiency of the query.

Using Splunk to query the system logs looking for transaction time and CPU usage (option A) is not a good solution, as it can be complex, costly, and difficult to integrate with Salesforce. It may also not provide enough information or insights to identify and optimize the query performance. Using Salesforce’s query optimizer to analyze the query in the developer console (option C) is also not a good solution, as it is not a separate tool that can be used in the developer console. The query optimizer is a feature that runs automatically when a SOQL query is executed and chooses the best query plan based on various factors2. Opening a ticket with Salesforce support to retrieve transaction logs to be analyzed for processing time (option D) is also not a good solution, as it can be time-consuming, dependent, and inefficient. It may also not provide enough information or insights to identify and optimize the query performance.

The data architect should recommend building a batch job to move two-year-old records off platform, and delete records from Salesforce as an archiving solution. A batch job is a process that runs in the background and performs operations on large volumes of data in Salesforce. By building a batch job that moves two-year-old records off platform to an external storage system, such as Amazon S3 or Google Cloud Storage, and deletes them from Salesforce, the data architect can reduce the storage consumption and improve the performance of Salesforce org. Option A is incorrect because using a third-party backup solution to backup all data off platform will not free up any storage space in Salesforce, unless the data is also deleted from Salesforce after backup. Option B is incorrect because building a batch job to move all records off platform, and delete all records from Salesforce will result in losing all the current data in Salesforce, which may not be desirable or feasible. Option D is incorrect because building a batch job to move all restore off platform, and delete old records from Salesforce does not make sense, as restore implies restoring data back to Salesforce, not moving it off platform.

When denormalizing a data model into a single object with a picklist field, the data architect should consider the Bulk API limitations on picklist fields and the standard list view in-line editing. The Bulk API has a limit of 1,000 distinct picklist values per file1, which could be an issue if there are more than 1,000 venues in the future. The standard list view in-line editing allows users to edit multiple records at once, which could introduce data quality issues if the venue picklist is not validated or restricted2. The other options are not relevant to denormalizing a data model.