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A table named user_ltv is being used to create a view that will be used by data analysis on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.

The user_ltv table has the following schema:

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An analyze who is not a member of the auditing group executing the following query:

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Which result will be returned by this query?

AAll columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.

BAll columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.

CAll age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.

DAll records from all columns will be displayed with the values in user_ltv.

A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impression led to monitizable clicks.

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Which solution would improve the performance?

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A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.

The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability.

Which piece of information is critical to this decision?

ADelta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.

BDelta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.

CShallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.

DData corruption can occur if a query fails in a partially completed state because Type 2 tables requires
Setting multiple fields in a single update.

A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.

Which consideration will impact the decisions made by the engineer while migrating this workload?

AAll Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.

BDatabricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.

CForeign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.

DCommitting to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.

The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.

Which of the following solutions addresses the situation while emphasizing simplicity?

ACreate a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.

BUse a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.

CAdd a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.

DCreate a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.

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