BigQuery for analytics in GCP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select all columns from a BigQuery table named 'sales_data'.

GCP

SELECT [1] FROM `project.dataset.sales_data`;

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

AEVERYTHING

B*

CCOLUMNS

DALL

Attempts:

3 left

💡 Hint

Common Mistakes

Using keywords like ALL or COLUMNS which are not valid in this context.

Trying to write out all column names manually.

The asterisk (*) selects all columns from the table in BigQuery SQL.

2fill in blank

medium

Complete the code to count the number of rows in the 'users' table.

GCP

SELECT COUNT([1]) FROM `project.dataset.users`;

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

Aall

B1

Cuser_id

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using a specific column name which counts only non-null values in that column.

Using '1' or 'all' which are not valid in COUNT function.

Using COUNT(*) counts all rows in the table regardless of column values.

3fill in blank

hard

Fix the error in the query to filter rows where 'age' is greater than 30.

GCP

SELECT * FROM `project.dataset.customers` WHERE age [1] 30;

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

A>

B=

C<

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' which filters only age exactly 30.

Using '<' or '<=' which filter smaller ages.

The '>' operator filters rows where age is greater than 30.

4fill in blank

hard

Fill both blanks to group sales by 'region' and calculate total revenue.

GCP

SELECT region, SUM([1]) AS total_revenue FROM `project.dataset.sales` GROUP BY [2];

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

Arevenue

Bregion

Csales

Dtotal

Attempts:

3 left

💡 Hint

Common Mistakes

Grouping by the wrong column.

Summing a column that does not represent revenue.

SUM(revenue) calculates total revenue, and GROUP BY region groups data by region.

5fill in blank

hard

Fill all three blanks to select user_id, count orders, and filter users with more than 5 orders.

GCP

SELECT [1], COUNT([2]) AS order_count FROM `project.dataset.orders` GROUP BY [3] HAVING order_count > 5;

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

Auser_id

B*

Dorder_id

Attempts:

3 left

💡 Hint

Common Mistakes

Counting the wrong column.

Grouping by a column not selected.

Select user_id, count order_id for orders, and group by user_id to filter users with more than 5 orders.

Practice

1. What is the main purpose of Google BigQuery in data analytics?

easy

A. To run fast SQL queries on large datasets without managing servers

B. To store files like images and videos

C. To create virtual machines for computing

D. To send emails automatically

Solution

1. Step 1: Understand BigQuery's role

   BigQuery is designed to analyze large amounts of data quickly using SQL without needing to manage servers.

2. Step 2: Compare options

   Options A, C, and D describe other services unrelated to BigQuery's main function.

3. Final Answer:

   To run fast SQL queries on large datasets without managing servers -> Option A

4. Quick Check:

   BigQuery = fast SQL analytics [OK]

Hint: BigQuery = fast SQL on big data, no servers needed [OK]

Common Mistakes:

• Confusing BigQuery with storage or compute services
• Thinking BigQuery manages virtual machines
• Assuming BigQuery sends emails

2. Which of the following is the correct syntax to select all columns from a table named sales_data in BigQuery?

easy

A. FETCH * sales_data;

B. GET ALL FROM sales_data;

C. SELECT * FROM sales_data;

D. SELECT ALL COLUMNS sales_data;

Solution

1. Step 1: Recall SQL SELECT syntax

   The correct SQL syntax to select all columns is SELECT * FROM table_name;.

2. Step 2: Check each option

   Only SELECT * FROM sales_data; matches the correct SQL syntax. Options A, B, and D use incorrect keywords or structure.

3. Final Answer:

   SELECT * FROM sales_data; -> Option C

4. Quick Check:

   SELECT * FROM table = correct syntax [OK]

Hint: Use SELECT * FROM table to get all columns [OK]

Common Mistakes:

• Using wrong keywords like GET or FETCH
• Omitting FROM keyword
• Writing SELECT ALL COLUMNS instead of SELECT *

3. Given the table orders with columns order_id, customer_id, and amount, what will this query return?

SELECT customer_id, SUM(amount) AS total_spent FROM orders GROUP BY customer_id ORDER BY total_spent DESC LIMIT 2;

medium

A. All orders sorted by amount

B. Top 2 customers who spent the most money

C. Total amount spent by all customers combined

D. First 2 orders by order_id

Solution

1. Step 1: Understand the query components

   The query groups orders by customer_id, sums the amount per customer, orders results by total spent descending, and limits output to 2 rows.

2. Step 2: Interpret the output

   This returns the top 2 customers who spent the most money.

3. Final Answer:

   Top 2 customers who spent the most money -> Option B

4. Quick Check:

   GROUP BY customer_id + SUM + ORDER BY DESC + LIMIT 2 = top 2 spenders [OK]

Hint: GROUP BY + SUM + ORDER BY DESC + LIMIT = top results [OK]

Common Mistakes:

• Thinking it returns all orders instead of grouped data
• Confusing LIMIT with total sum
• Ignoring ORDER BY direction

4. You wrote this query but get an error:

SELECT customer_id, amount FROM orders WHERE SUM(amount) > 100;

What is the problem?

medium

A. SELECT must include all columns in WHERE

B. Missing GROUP BY clause for customer_id

C. amount column does not exist

D. SUM() cannot be used in WHERE clause; use HAVING instead

Solution

1. Step 1: Identify misuse of SUM() in WHERE

   Aggregate functions like SUM() cannot be used in WHERE; they belong in HAVING clause.

2. Step 2: Correct usage

   Replace WHERE SUM(amount) > 100 with HAVING SUM(amount) > 100 and add GROUP BY customer_id.

3. Final Answer:

   SUM() cannot be used in WHERE clause; use HAVING instead -> Option D

4. Quick Check:

   Aggregates in HAVING, not WHERE [OK]

Hint: Use HAVING for conditions on aggregates, not WHERE [OK]

Common Mistakes:

• Using SUM() in WHERE clause
• Forgetting GROUP BY with aggregates
• Assuming WHERE filters aggregates

5. You want to find the average order amount per customer but only for customers who placed more than 5 orders. Which query correctly achieves this in BigQuery?

hard

A. SELECT customer_id, AVG(amount) AS avg_amount FROM orders GROUP BY customer_id HAVING COUNT(order_id) > 5;

B. SELECT customer_id, AVG(amount) AS avg_amount FROM orders WHERE COUNT(order_id) > 5 GROUP BY customer_id;

C. SELECT customer_id, AVG(amount) AS avg_amount FROM orders WHERE order_count > 5;

D. SELECT customer_id, AVG(amount) AS avg_amount FROM orders GROUP BY customer_id WHERE COUNT(order_id) > 5;

Solution

1. Step 1: Understand filtering on aggregate

   To filter groups by count, use HAVING clause after GROUP BY.

2. Step 2: Check each option's correctness

   SELECT customer_id, AVG(amount) AS avg_amount FROM orders GROUP BY customer_id HAVING COUNT(order_id) > 5; uses GROUP BY customer_id and HAVING COUNT(order_id) > 5 correctly. Options A, B, and C misuse WHERE or clause order.

3. Final Answer:

   SELECT customer_id, AVG(amount) AS avg_amount FROM orders GROUP BY customer_id HAVING COUNT(order_id) > 5; -> Option A

4. Quick Check:

   HAVING filters groups, WHERE filters rows [OK]

Hint: Use HAVING to filter groups after GROUP BY [OK]

Common Mistakes:

• Using WHERE to filter aggregates
• Placing WHERE after GROUP BY
• Referencing undefined columns like order_count