Looker for visualization in GCP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Looker dashboard with a title.

GCP

dashboard = looker.create_dashboard(title=[1])

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

A12345

BSales Overview

Cdashboard_title

D"Sales Overview"

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the title

Using a variable name instead of a string

The title must be a string enclosed in quotes.

2fill in blank

medium

Complete the code to add a Looker tile with a query ID.

GCP

tile = dashboard.add_tile(query_id=[1])

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

Aquery123

B"query123"

Ctile1

D123query

Attempts:

3 left

💡 Hint

Common Mistakes

Using unquoted query ID

Using an invalid ID format

The query ID must be a string, so it needs quotes.

3fill in blank

hard

Fix the error in setting the visualization type for a Looker tile.

GCP

tile.set_visualization_type([1])

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

A"bar_chart"

Bbar_chart

CBarChart

DbarChart

Attempts:

3 left

💡 Hint

Common Mistakes

Missing quotes

Using camelCase or PascalCase instead of snake_case

The visualization type must be a string with quotes and lowercase with underscores.

4fill in blank

hard

Fill both blanks to filter a Looker query for sales greater than 1000.

GCP

query.set_filter(field=[1], value=[2])

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

A"sales"

B1000

C"region"

D500

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong field name

Putting numbers in quotes

The field name must be "sales" as a string, and the value must be 1000 without quotes.

5fill in blank

hard

Fill all three blanks to create a Looker visualization with a title, type, and filter.

GCP

dashboard = looker.create_dashboard(title=[1])
tile = dashboard.add_tile(query_id=[2])
tile.set_visualization_type([3])

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

A"Monthly Sales"

B"query789"

C"line_chart"

D"query123"

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong query ID

Missing quotes

Wrong visualization type format

The dashboard title is a string, the query ID must match an existing query string, and the visualization type must be a string with the correct format.

Practice

1. What is the main purpose of Looker in data analysis?

easy

A. To write complex SQL queries manually

B. To create visual reports from raw data without coding

C. To store large amounts of raw data

D. To replace databases with spreadsheets

Solution

1. Step 1: Understand Looker's role

   Looker is designed to turn raw data into visual reports easily.

2. Step 2: Compare options

   Only To create visual reports from raw data without coding describes Looker's main function correctly; others describe unrelated tasks.

3. Final Answer:

   To create visual reports from raw data without coding -> Option B

4. Quick Check:

   Looker = Visual reports without coding [OK]

Hint: Looker = easy visuals from data, no coding needed [OK]

Common Mistakes:

• Thinking Looker stores data like a database
• Confusing Looker with manual SQL writing
• Assuming Looker replaces spreadsheets

2. Which syntax correctly defines a measure in LookML?

easy

A. measure total_sales { sum: sales }

B. measure total_sales = sum(${TABLE}.sales)

C. measure: total_sales { type: sum; sql: ${TABLE}.sales ;; }

D. measure: total_sales = sum(sales)

Solution

1. Step 1: Recall LookML measure syntax

   Measures use the format: measure: name { type: ...; sql: ... ;; }

2. Step 2: Check each option

   measure: total_sales { type: sum; sql: ${TABLE}.sales ;; } matches correct LookML syntax with colons and braces; others do not.

3. Final Answer:

   measure: total_sales { type: sum; sql: ${TABLE}.sales ;; } -> Option C

4. Quick Check:

   LookML measure syntax uses colons and braces [OK]

Hint: LookML uses colons and braces for measures [OK]

Common Mistakes:

• Using equal signs instead of colons
• Omitting braces {} around measure definition
• Incorrect SQL syntax inside measure

3. Given this LookML snippet, what will the measure average_price calculate?

measure: average_price {
  type: average
  sql: ${TABLE}.price ;; }

medium

A. Sum of all prices

B. Count of price entries

C. Maximum price value

D. Average of the price column

Solution

1. Step 1: Identify measure type

   The measure type is 'average', which calculates the mean value.

2. Step 2: Understand SQL reference

   The SQL references the price column, so it averages all prices.

3. Final Answer:

   Average of the price column -> Option D

4. Quick Check:

   Measure type 'average' = mean value [OK]

Hint: Measure type 'average' means mean value [OK]

Common Mistakes:

• Confusing average with sum or count
• Ignoring the measure type keyword
• Assuming it returns max value

4. You wrote this LookML measure but get an error:

measure: total_revenue {
  type: sum
  sql: ${revenue} ;; }

What is the likely cause?

medium

A. Missing ${TABLE} prefix before revenue

B. Incorrect measure type 'sum'

C. Extra semicolon after sql statement

D. Measure name cannot have underscores

Solution

1. Step 1: Check SQL reference format

   LookML requires ${TABLE}.column to reference table columns properly.

2. Step 2: Identify missing prefix

   The sql references ${revenue} without ${TABLE}., causing error.

3. Final Answer:

   Missing ${TABLE} prefix before revenue -> Option A

4. Quick Check:

   Column refs need ${TABLE}. prefix [OK]

Hint: Always prefix columns with ${TABLE}. in LookML SQL [OK]

Common Mistakes:

• Omitting ${TABLE}. before column names
• Thinking measure type 'sum' is invalid
• Assuming underscores are not allowed in names

5. You want to create a dashboard showing total sales and average order value per region. Which LookML approach is best?

hard

A. Define measures for total sales and average order value, then create an explore with region dimension

B. Write raw SQL queries for each metric and paste results manually into dashboard

C. Create separate dashboards for sales and orders without linking regions

D. Use only dimensions without measures to keep dashboard simple

Solution

1. Step 1: Identify needed metrics and grouping

   You need total sales and average order value as measures, grouped by region dimension.

2. Step 2: Use LookML best practice

   Defining measures and an explore with region dimension allows dynamic, reusable dashboards.

3. Step 3: Evaluate other options

   The other options do not provide integrated, maintainable visualization.

4. Final Answer:

   Define measures for total sales and average order value, then create an explore with region dimension -> Option A

5. Quick Check:

   Measures + dimension + explore = best dashboard [OK]

Hint: Use measures + dimension + explore for dashboards [OK]

Common Mistakes:

• Manually copying SQL results instead of using LookML
• Separating dashboards without common grouping
• Ignoring measures and using only dimensions