Why NoSQL on GCP matters - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to access a Firestore collection using the GCP client.

GCP

gcp_client = firestore.Client()
db = gcp_client.[1]('users')

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

Acollection

Bcreate_instance

Cdelete_instance

Dupdate_instance

Attempts:

3 left

💡 Hint

Common Mistakes

Using methods that create or delete instances instead of accessing collections.

The collection() method is used to access a NoSQL collection in Firestore.

2fill in blank

medium

Complete the code to add a document to a Firestore collection.

GCP

doc_ref = db.[1]('users').document('user1')
doc_ref.set({'name': 'Alice', 'age': 30})

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

Aget

Bupdate

Cdelete

Dcollection

Attempts:

3 left

💡 Hint

Common Mistakes

Using get or delete which do not create collections.

The collection() method accesses the 'users' collection to add a document.

3fill in blank

hard

Fix the error in the Firestore query to retrieve documents where age is greater than 25.

GCP

query = db.collection('users').where('age', '[1]', 25)

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

A=

B<

C>

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' or '<' which filter for equality or less than, not greater than.

The operator '>' is used to filter documents where age is greater than 25.

4fill in blank

hard

Fill both blanks to create a Firestore document update that changes the user's age and adds a new field.

GCP

doc_ref.update({'[1]': 31, '[2]': 'active'})

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

Aage

Bstatus

Cname

Demail

Attempts:

3 left

💡 Hint

Common Mistakes

Using fields unrelated to the update like 'email' or 'name'.

The fields age and status are updated in the document.

5fill in blank

hard

Fill all three blanks to create a Firestore query that orders users by age descending and limits results to 5.

GCP

query = db.collection('[1]').order_by('[2]', direction='[3]').limit(5)

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

Ausers

Bage

CDESCENDING

Dname

Attempts:

3 left

💡 Hint

Common Mistakes

Ordering by 'name' or using ascending direction.

The query accesses the 'users' collection, orders by 'age' descending, and limits to 5 results.

Practice

1. Why is NoSQL often chosen on Google Cloud Platform (GCP) for certain applications?

easy

A. Because it handles flexible and rapidly changing data well

B. Because it requires complex server management

C. Because it only supports structured data

D. Because it is slower than traditional databases

Solution

1. Step 1: Understand NoSQL data handling

   NoSQL databases are designed to store flexible, semi-structured, or unstructured data that can change quickly.

2. Step 2: Relate to GCP use case

   On GCP, NoSQL services like Firestore and Bigtable support fast-changing data without rigid schemas.

3. Final Answer:

   Because it handles flexible and rapidly changing data well -> Option A

4. Quick Check:

   NoSQL = flexible, fast-changing data [OK]

Hint: NoSQL = flexible data, not rigid tables [OK]

Common Mistakes:

• Thinking NoSQL requires complex server management
• Assuming NoSQL only supports structured data
• Believing NoSQL is slower than SQL

2. Which GCP NoSQL service is best described as a document database?

easy

A. Cloud Spanner

B. Cloud Bigtable

C. Cloud SQL

D. Cloud Firestore

Solution

1. Step 1: Identify document database on GCP

   Cloud Firestore stores data as documents in collections, making it a document database.

2. Step 2: Differentiate from other services

   Cloud Bigtable is a wide-column store, Cloud SQL and Spanner are relational databases.

3. Final Answer:

   Cloud Firestore -> Option D

4. Quick Check:

   Document DB on GCP = Firestore [OK]

Hint: Firestore = document DB, Bigtable = wide-column [OK]

Common Mistakes:

• Confusing Bigtable as document DB
• Choosing relational DBs like Cloud SQL
• Mixing Spanner with NoSQL services

3. Given this code snippet using Firestore on GCP:

doc_ref = firestore_client.collection('users').document('user123')
doc_ref.set({'name': 'Alice', 'age': 30})
user = doc_ref.get().to_dict()
print(user['name'])

What will be printed?

medium

A. user123

B. Alice

C. 30

D. None

Solution

1. Step 1: Understand Firestore document creation

   The code sets a document with fields 'name' and 'age' under 'users/user123'.

2. Step 2: Retrieve and print the 'name' field

   The get() call fetches the document, and user['name'] accesses the 'name' value, which is 'Alice'.

3. Final Answer:

   Alice -> Option B

4. Quick Check:

   Print user['name'] = Alice [OK]

Hint: Set then get document fields to print values [OK]

Common Mistakes:

• Printing document ID instead of field
• Confusing field names
• Assuming None if document exists

4. You try to write data to Cloud Bigtable but get an error. Which mistake likely caused it?

table = bigtable_client.table('my-table')
row = table.row('row1')
row.set_cell('cf1', 'name', 'Bob')
row.commit()

medium

A. Missing column family 'cf1' creation before setting cell

B. Using wrong row key format

C. Calling commit() before set_cell()

D. Bigtable does not support string values

Solution

1. Step 1: Check Bigtable column family setup

   Bigtable requires column families to exist before writing cells; 'cf1' must be created first.

2. Step 2: Verify code sequence and data types

   Row key format is valid, commit() is correctly called after set_cell(), and Bigtable supports string values.

3. Final Answer:

   Missing column family 'cf1' creation before setting cell -> Option A

4. Quick Check:

   Column family must exist before writing [OK]

Hint: Create column family before writing cells [OK]

Common Mistakes:

• Forgetting to create column family
• Assuming commit() order is wrong
• Thinking Bigtable rejects strings

5. You want to build a scalable app on GCP that needs to store user profiles with flexible fields and also handle millions of fast writes per second. Which combination of NoSQL services should you choose?

hard

A. Use Cloud SQL for profiles and Cloud Spanner for writes

B. Use Cloud Bigtable for profiles and Cloud Firestore for writes

C. Use Cloud Firestore for flexible profiles and Cloud Bigtable for high-speed writes

D. Use Cloud Spanner for both profiles and writes

Solution

1. Step 1: Match data needs to NoSQL services

   Cloud Firestore handles flexible, document-based user profiles well.

2. Step 2: Match write speed needs

   Cloud Bigtable supports massive scale and very fast writes, ideal for high throughput.

3. Step 3: Evaluate other options

   Cloud SQL and Spanner are relational and less suited for flexible schema or extreme write speed.

4. Final Answer:

   Use Cloud Firestore for flexible profiles and Cloud Bigtable for high-speed writes -> Option C

5. Quick Check:

   Firestore + Bigtable = flexible + fast writes [OK]

Hint: Firestore for flexible data, Bigtable for speed [OK]

Common Mistakes:

• Choosing relational DBs for flexible schema
• Swapping Firestore and Bigtable roles
• Ignoring write speed requirements