Metric definitions and semantic layer in dbt - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a simple metric that counts orders.

dbt

metrics:
  - name: order_count
    model: orders
    label: "Order Count"
    type: [1]

Drag options to blanks, or click blank then click option'

Amax

Bsum

Caverage

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum instead of count for counting rows.

Using average or max which are for numeric aggregations.

The count type counts the number of rows, which is suitable for counting orders.

2fill in blank

medium

Complete the code to define a metric that sums the revenue column.

dbt

metrics:
  - name: total_revenue
    model: sales
    label: "Total Revenue"
    type: [1]
    sql: revenue

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Asum

Baverage

Ccount

Dmin

Attempts:

3 left

💡 Hint

Common Mistakes

Using count which counts rows, not sums values.

Using average or min which calculate different statistics.

The sum type adds up all values in the revenue column, giving total revenue.

3fill in blank

hard

Fix the error in the metric definition by completing the missing field.

dbt

metrics:
  - name: average_order_value
    model: orders
    label: "Average Order Value"
    type: sum
    sql: total_amount
    [1]: order_count

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Aexpression

Bfilter

Ctimestamp

Ddrill_fields

Attempts:

3 left

💡 Hint

Common Mistakes

Using filter or timestamp which are unrelated here.

Using drill_fields which is for navigation, not calculation.

The expression field defines how to calculate the metric, such as dividing sum by count for average.

4fill in blank

hard

Fill both blanks to create a semantic layer metric that filters by date and groups by customer.

dbt

metrics:
  - name: monthly_revenue
    model: sales
    label: "Monthly Revenue"
    type: sum
    sql: amount
    filters:
      - field: [1]
        operator: >=
        value: '2024-01-01'
    dimensions:
      - [2]

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Aorder_date

Bcustomer_id

Camount

Dregion

Attempts:

3 left

💡 Hint

Common Mistakes

Filtering on amount instead of date.

Grouping by amount or region instead of customer.

The filter uses order_date to select recent data, and grouping is done by customer_id to see revenue per customer.

5fill in blank

hard

Fill all three blanks to define a metric with a time grain, filter, and label.

dbt

metrics:
  - name: daily_active_users
    model: user_activity
    label: [1]
    type: count
    time_grains:
      - [2]
    filters:
      - field: [3]
        operator: =
        value: 'active'

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A"Daily Active Users"

Bday

Cstatus

Dmonth

Attempts:

3 left

💡 Hint

Common Mistakes

Using month instead of day for daily metrics.

Filtering on wrong field instead of status.

The label is "Daily Active Users", the time grain is day for daily counts, and the filter uses the status field to select active users.