MongoDB Associate Developer - Fact Sheet¶

Exam Overview¶

Exam Name: MongoDB Associate Developer Duration: 75 minutes Questions: 62 multiple-choice questions Passing Score: 65% Cost: $150 USD Valid For: 3 years Delivery: Online proctored

📖 Official Certification Page - Registration and details 📖 MongoDB Documentation - Complete reference 📖 MongoDB University - Free learning resources

Target Audience¶

This certification is designed for: - Application developers building with MongoDB - Backend engineers using MongoDB for data storage - Full-stack developers working with document databases - Data engineers designing MongoDB schemas - Professionals with 6+ months MongoDB development experience

Exam Domains¶

Domain 1: CRUD Operations (30%)¶

Insert Operations: - db.collection.insertOne({doc}) - Insert single document - db.collection.insertMany([{doc1}, {doc2}]) - Insert multiple documents - _id is auto-generated as ObjectId if not specified - ordered: false option continues on error for insertMany

Query Operations: - db.collection.find({filter}, {projection}) - Find matching documents - db.collection.findOne({filter}) - Find first matching document - Cursor methods: .sort(), .limit(), .skip(), .count()

Query Operators: | Operator | Description | Example | |----------|-------------|---------| | $eq | Equal | {age: {$eq: 25}} | | $ne | Not equal | {status: {$ne: "inactive"}} | | $gt / $gte | Greater than (or equal) | {age: {$gt: 18}} | | $lt / $lte | Less than (or equal) | {price: {$lt: 100}} | | $in | In array | {status: {$in: ["A", "B"]}} | | $nin | Not in array | {status: {$nin: ["D"]}} | | $and | Logical AND | {$and: [{a: 1}, {b: 2}]} | | $or | Logical OR | {$or: [{a: 1}, {b: 2}]} | | $not | Logical NOT | {age: {$not: {$gt: 25}}} | | $exists | Field exists | {email: {$exists: true}} | | $type | Field type | {age: {$type: "number"}} | | $regex | Regular expression | {name: {$regex: /^J/}} | | $elemMatch | Array element match | {scores: {$elemMatch: {$gt: 80}}} | | $all | All elements match | {tags: {$all: ["a", "b"]}} | | $size | Array size | {tags: {$size: 3}} |

Update Operators: | Operator | Description | Example | |----------|-------------|---------| | $set | Set field value | {$set: {name: "new"}} | | $unset | Remove field | {$unset: {temp: ""}} | | $inc | Increment value | {$inc: {count: 1}} | | $mul | Multiply value | {$mul: {price: 1.1}} | | $rename | Rename field | {$rename: {old: "new"}} | | $min / $max | Update if less/greater | {$min: {low: 5}} | | $push | Add to array | {$push: {tags: "new"}} | | $pull | Remove from array | {$pull: {tags: "old"}} | | $addToSet | Add unique to array | {$addToSet: {tags: "x"}} | | $pop | Remove first/last array element | {$pop: {tags: 1}} | | $each | Modifier for $push/$addToSet | {$push: {tags: {$each: ["a","b"]}}} |

📖 CRUD Reference - Complete CRUD documentation

Domain 2: Indexes (15%)¶

Index Types: - Single Field - Index on one field: db.col.createIndex({field: 1}) - Compound - Multiple fields: db.col.createIndex({a: 1, b: -1}) - Multikey - Automatically created for array fields - Text - Full-text search: db.col.createIndex({content: "text"}) - Geospatial - 2d and 2dsphere indexes for location queries - Hashed - Hash-based: db.col.createIndex({field: "hashed"}) - TTL - Auto-delete documents: db.col.createIndex({created: 1}, {expireAfterSeconds: 3600}) - Unique - Enforce uniqueness: db.col.createIndex({email: 1}, {unique: true}) - Sparse - Only index documents with the field: {sparse: true} - Partial - Index subset: {partialFilterExpression: {status: "active"}}

ESR Rule for Compound Indexes: - Equality fields first (exact match) - Sort fields next (ordering) - Range fields last (inequality, $gt, $lt)

📖 Indexes - Index documentation

Domain 3: Data Modeling (20%)¶

Embedding vs Referencing: | Factor | Embed | Reference | |--------|-------|-----------| | Read frequency together | High | Low | | Data size | Small | Large | | Update frequency | Low | High | | Relationship | One-to-few | One-to-many (large) | | Atomicity needed | Yes | No |

Document Size Limit: 16 MB maximum per document

Schema Patterns: - Polymorphic - Different document shapes in same collection - Bucket - Group time-series data into buckets - Outlier - Handle documents with unusual array sizes - Attribute - Convert many similar fields to key-value pairs - Subset - Store frequently accessed subset in main document - Extended Reference - Copy frequently accessed fields from referenced document - Computed - Pre-compute and store derived values

📖 Data Modeling - Schema design guide

Domain 4: Aggregation (25%)¶

Key Pipeline Stages: | Stage | Description | |-------|-------------| | $match | Filter documents (like find) | | $group | Group by field, compute aggregates | | $project | Reshape documents, include/exclude fields | | $sort | Sort documents | | $limit / $skip | Pagination | | $lookup | Left outer join with another collection | | $unwind | Deconstruct array into separate documents | | $addFields / $set | Add computed fields | | $count | Count documents | | $facet | Multiple aggregation pipelines in parallel | | $bucket | Categorize into buckets | | $replaceRoot | Replace document with subdocument | | $merge / $out | Write results to collection | | $graphLookup | Recursive graph traversal |

📖 Aggregation Pipeline - Pipeline reference

Domain 5: Atlas Tools (10%)¶

Atlas Search: - Full-text search powered by Apache Lucene - Search indexes defined on collections - Operators: text, phrase, autocomplete, compound, range - Accessed via $search aggregation stage

Atlas Charts: - Built-in data visualization tool - Connects directly to Atlas collections - Chart types: bar, line, scatter, heatmap, geo, etc.

Atlas Data API: - REST API for CRUD operations - HTTP endpoints for find, insert, update, delete - Authentication via API keys

Atlas Triggers: - Database triggers (on insert/update/delete) - Scheduled triggers (cron-based) - Authentication triggers (on user events)

📖 Atlas Search - Atlas Search documentation 📖 Atlas Triggers - Triggers documentation

Key Limits to Remember¶