When adding fields from a dimension_group to a set, how often do you need to add each time frame dimension?

When adding fields from a dimension_group to a set, how often do you need to add each time frame dimension?

• A. Only once
• B. Each time frame dimension individually
• C. At least twice
• D. Once per set

Answer: (B)

The correct answer is that you need to add each time frame dimension individually when including fields from a dimension_group into a set. This is because dimension_groups in Looker are designed to represent multiple time frames—such as year, month, day, etc.—and each of these dimensions operates independently. When you create a set in LookML that references a dimension_group, you must explicitly define each individual time frame dimension you want to include in the set. This ensures that the specific time periods (such as year, month, and day) can be selected or applied separately as needed, allowing for greater flexibility in analysis and reporting. This approach also helps maintain clarity and precision in your LookML modeling, as it minimizes confusion regarding which time frames are actually being referenced in reports or queries. By adding each time frame dimension individually, you ensure that your data model accurately reflects the analytical needs of users.

The correct answer is that you need to add each time frame dimension individually when including fields from a dimension_group into a set. This is because dimension_groups in Looker are designed to represent multiple time frames—such as year, month, day, etc.—and each of these dimensions operates independently.

When you create a set in LookML that references a dimension_group, you must explicitly define each individual time frame dimension you want to include in the set. This ensures that the specific time periods (such as year, month, and day) can be selected or applied separately as needed, allowing for greater flexibility in analysis and reporting.

This approach also helps maintain clarity and precision in your LookML modeling, as it minimizes confusion regarding which time frames are actually being referenced in reports or queries. By adding each time frame dimension individually, you ensure that your data model accurately reflects the analytical needs of users.