This lab combines the concepts from the chapter into a small learning-platform dataset. Run the commands in mongosh against a development database.

Step 1: Select the practice database

use mongodb_course_lab

Step 2: Insert sample courses

db.courses.insertMany([
  {
    slug: "mongodb-basics",
    title: "MongoDB Basics",
    category: "database",
    level: "beginner",
    durationMinutes: 120,
    published: true,
    tags: ["mongodb", "nosql", "backend"],
    instructor: {
      name: "Kavitha",
      country: "India"
    },
    createdAt: ISODate("2026-10-01T09:00:00Z")
  },
  {
    slug: "mongodb-querying",
    title: "MongoDB Querying in Depth",
    category: "database",
    level: "intermediate",
    durationMinutes: 240,
    published: true,
    tags: ["mongodb", "queries", "backend"],
    instructor: {
      name: "Arun",
      country: "India"
    },
    createdAt: ISODate("2026-10-05T09:00:00Z")
  },
  {
    slug: "react-foundations",
    title: "React Foundations",
    category: "frontend",
    level: "beginner",
    durationMinutes: 180,
    published: false,
    tags: ["react", "javascript", "frontend"],
    instructor: {
      name: "Meera",
      country: "India"
    },
    createdAt: ISODate("2026-10-08T09:00:00Z")
  },
  {
    slug: "mongodb-data-modeling",
    title: "MongoDB Data Modeling",
    category: "database",
    level: "advanced",
    durationMinutes: 360,
    published: true,
    tags: ["mongodb", "schema-design", "architecture"],
    instructor: {
      name: "Kavitha",
      country: "India"
    },
    createdAt: ISODate("2026-10-12T09:00:00Z")
  }
]);

Step 3: Inspect the inserted records

db.courses.find();

Observe the _id values generated by MongoDB. Inspect the arrays, nested instructor documents, dates, and scalar fields.

Step 4: Filter by category

db.courses.find({ category: "database" });

This query returns the database courses. It does not require every document in the collection to belong to that category.

Step 5: Query a nested field

db.courses.find({ "instructor.name": "Kavitha" });

The query uses dot notation to select courses taught by Kavitha.

Step 6: Filter by multiple conditions

db.courses.find({
  category: "database",
  published: true
});

Multiple fields in a standard query filter are combined using logical AND. A document must satisfy both conditions to match.

Step 7: Return selected fields

db.courses.find(
  { published: true },
  { _id: 0, title: 1, level: 1, durationMinutes: 1 }
);

The first argument specifies which documents to match. The second argument specifies which fields to return. This projection excludes _id and includes the three requested fields.

Step 8: Count published courses

db.courses.countDocuments({ published: true });

With the supplied data, this should return 3.

Step 9: Inspect a date range

db.courses.find({
  createdAt: {
    $gte: ISODate("2026-10-05T00:00:00Z"),
    $lt: ISODate("2026-10-13T00:00:00Z")
  }
});

This query uses an inclusive lower bound and an exclusive upper bound. It returns documents created on or after October 5 and before October 13, 2026, according to the stored UTC dates.

Step 10: Clean up when finished

If this is a disposable practice database and you want to remove the lab collection, run:

db.courses.drop();

This deletes the collection and its documents. Do not run destructive commands against a production collection.

Lab challenges

1. Find every course whose level is beginner.
2. Find published courses taught by Arun.
3. Return only slug and title for frontend courses.
4. Count all courses in the database category.
5. Find courses whose tags contain backend.
6. Find courses created after October 5, 2026.
7. Explain why instructor.name is a nested field path rather than a separate top-level field.
8. Describe one reason you might keep enrollments in a separate collection rather than embedding an unbounded enrollment history inside each course.