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MongoDBquery~10 mins

Why document design matters in MongoDB - Test Your Understanding

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Test this pattern10 questions across easy, medium, and hard to know if this pattern is strong
Practice - 5 Tasks
Answer the questions below
1fill in blank
easy

Complete the code to insert a document into the collection.

MongoDB
db.users.insertOne({ name: "Alice", age: [1] })
Drag options to blanks, or click blank then click option'
Anull
B"25"
Cage
D25
Attempts:
3 left
💡 Hint
Common Mistakes
Putting the number 25 in quotes, making it a string.
Using a variable name instead of a value.
2fill in blank
medium

Complete the code to find documents where the age is greater than 30.

MongoDB
db.users.find({ age: { [1]: 30 } })
Drag options to blanks, or click blank then click option'
A$gt
B$lt
C$eq
D$ne
Attempts:
3 left
💡 Hint
Common Mistakes
Using $lt which means less than.
Using $eq which means equal to.
3fill in blank
hard

Fix the error in the update query to set the city field.

MongoDB
db.users.updateOne({ name: "Bob" }, { [1]: { city: "Paris" } })
Drag options to blanks, or click blank then click option'
A$update
Bset
C$set
Dupdate
Attempts:
3 left
💡 Hint
Common Mistakes
Leaving out the dollar sign in the operator.
Using a wrong operator like $update.
4fill in blank
hard

Fill both blanks to create a document with an embedded address object.

MongoDB
db.users.insertOne({ name: "Eve", address: { [1]: "123 Main St", [2]: "NY" } })
Drag options to blanks, or click blank then click option'
Astreet
Bcity
Cstate
Dzipcode
Attempts:
3 left
💡 Hint
Common Mistakes
Using city instead of state for the second blank.
Using zipcode which is not in the example.
5fill in blank
hard

Fill all three blanks to query documents with age greater than 20 and city equals 'Boston'.

MongoDB
db.users.find({ age: { [1]: 20 }, address: { [2]: [3] } })
Drag options to blanks, or click blank then click option'
A$gt
Bcity
C"Boston"
D$lt
Attempts:
3 left
💡 Hint
Common Mistakes
Using $lt instead of $gt for age.
Not putting Boston in quotes.
Using wrong field name instead of city.

Practice

(1/5)
1. Why is good document design important in MongoDB?
easy
A. It groups related data together for faster access.
B. It makes the database use more disk space.
C. It forces all data to be stored in separate collections.
D. It prevents any data from being updated.

Solution

  1. Step 1: Understand document design purpose

    Good document design groups related data to reduce the number of database lookups.
  2. Step 2: Identify the benefit of grouping data

    Grouping related data together makes data access faster and simpler for the application.
  3. Final Answer:

    It groups related data together for faster access. -> Option A
  4. Quick Check:

    Good design = grouped data = faster access [OK]
Hint: Good design groups related data for speed [OK]
Common Mistakes:
  • Thinking design increases disk space unnecessarily
  • Believing all data must be in separate collections
  • Assuming design stops data updates
2. Which of the following is the correct way to embed an address inside a user document in MongoDB?
easy
A. { name: 'Alice', address: ['NY', '10001'] }
B. { name: 'Alice', address: { city: 'NY', zip: '10001' } }
C. { name: 'Alice', address: 'NY, 10001' }
D. { name: 'Alice', address: null }

Solution

  1. Step 1: Recognize embedded document syntax

    Embedding means putting a document inside another document as a nested object.
  2. Step 2: Identify correct nested object format

    { name: 'Alice', address: { city: 'NY', zip: '10001' } } uses a nested object with keys city and zip, which is correct for embedding.
  3. Final Answer:

    { name: 'Alice', address: { city: 'NY', zip: '10001' } } -> Option B
  4. Quick Check:

    Embedded document = nested object = { name: 'Alice', address: { city: 'NY', zip: '10001' } } [OK]
Hint: Embed data as nested objects, not arrays or strings [OK]
Common Mistakes:
  • Using arrays instead of objects for embedded data
  • Storing address as a plain string
  • Leaving embedded fields null without reason
3. Given this user document:
{ _id: 1, name: 'Bob', orders: [{ id: 101, total: 50 }, { id: 102, total: 30 }] }
What will be the result of the query db.users.findOne({ _id: 1 })?
medium
A. null
B. { _id: 1, name: 'Bob' }
C. { _id: 1, name: 'Bob', orders: [{ id: 101, total: 50 }, { id: 102, total: 30 }] }
D. SyntaxError

Solution

  1. Step 1: Understand findOne query behavior

    The findOne query returns the entire document matching the filter {_id: 1}.
  2. Step 2: Check document structure

    The document includes the orders array embedded inside, so the full document is returned.
  3. Final Answer:

    { _id: 1, name: 'Bob', orders: [{ id: 101, total: 50 }, { id: 102, total: 30 }] } -> Option C
  4. Quick Check:

    findOne returns full document = { _id: 1, name: 'Bob', orders: [{ id: 101, total: 50 }, { id: 102, total: 30 }] } [OK]
Hint: findOne returns full matching document [OK]
Common Mistakes:
  • Expecting only part of the document returned
  • Thinking query returns null if embedded arrays exist
  • Confusing syntax errors with valid queries
4. You want to embed a list of comments inside a blog post document, but your code throws an error. Which is the likely cause?
{ title: 'Post', comments: 'Great post!' }
medium
A. Comments should be an array of objects, not a string.
B. Title field cannot be a string.
C. MongoDB does not allow embedding arrays.
D. The document must have an _id field.

Solution

  1. Step 1: Check the comments field type

    Comments are given as a string, but embedding multiple comments requires an array of objects.
  2. Step 2: Understand embedding requirements

    Embedding multiple related items means using an array of objects, not a single string.
  3. Final Answer:

    Comments should be an array of objects, not a string. -> Option A
  4. Quick Check:

    Embed lists as arrays, not strings [OK]
Hint: Embed lists as arrays of objects, not strings [OK]
Common Mistakes:
  • Using string instead of array for multiple items
  • Thinking title cannot be string
  • Believing MongoDB forbids arrays
  • Assuming _id is always required manually
5. You have a product catalog where each product has many reviews. Reviews can grow large over time. What is the best document design to handle this efficiently?
hard
A. Embed all reviews inside each product document.
B. Duplicate product data inside each review document.
C. Store only the first review inside the product document.
D. Store reviews in a separate collection linked by product ID.

Solution

  1. Step 1: Consider document size limits and growth

    Embedding many reviews inside a product can make the document very large and slow to update.
  2. Step 2: Choose design for large growing data

    Storing reviews separately and linking by product ID keeps product documents small and queries efficient.
  3. Final Answer:

    Store reviews in a separate collection linked by product ID. -> Option D
  4. Quick Check:

    Large growing data = separate collection [OK]
Hint: Large growing lists? Use separate collections [OK]
Common Mistakes:
  • Embedding large growing arrays inside documents
  • Storing only partial data inside main document
  • Duplicating product data unnecessarily