MongoDB Associate Developer study plan
MongoDB Associate Developer · Give each week a clear focus.
8 weeks · About 7 hours per week
MongoDB Overview and Architecture & CRUD Operations
Diagram the MongoDB deployment topology - mongod, mongos, config servers, and replica set members - and state the role each plays in a read or write path
Describe how the WiredTiger storage engine organizes collections and indexes, and why BSON types (ObjectId, Date, Decimal128) matter for application correctness
Run insertOne/insertMany, find with filter and projection, updateOne/updateMany, and deleteOne/deleteMany against a local mongod from mongosh
Choose between an embedded document and a reference for a stated access pattern and justify the choice in one sentence
Your checkpoint
A local database with two related collections and a saved mongosh script that executes at least 10 distinct CRUD operations and prints each result.
CRUD Operations & Indexes and Query Performance
Compose queries using comparison operators, $and/$or/$not, $in, $exists, dot notation on embedded fields, and array-match semantics (scalar match vs $elemMatch)
Apply update operators $set, $unset, $inc, $push, $addToSet, $pull, and $pop, including upsert:true and arrayFilters on nested arrays
Create single-field, compound, multikey, unique, and partial indexes and state what each does to write cost
Explain why equality fields precede sort fields precede range fields in a compound index (ESR) and reorder an index to match a given query
Your checkpoint
A 10,000-document dataset with four indexes, plus a query log of 10 queries each run through explain('executionStats') showing IXSCAN rather than COLLSCAN.
Indexes and Query Performance
Read explain('executionStats') output and identify stage names, keysExamined, docsExamined, nReturned, and totalKeysExamined
Diagnose three deliberately slow queries and fix each by adding, reordering, or covering with an index
Decide when a covering index applies and rewrite a query's projection so the index alone satisfies it
Select among TTL, sparse, partial, wildcard, and text indexes for four stated requirements and name the trade-off of each
Explain how the in-memory sort limit and low-cardinality keys affect whether the planner picks an index
Your checkpoint
Before-and-after explain plans for five queries, each annotated with the change made and the reduction in the keysExamined-to-nReturned ratio.
Aggregation Framework
Build pipelines using $match, $project, $addFields, $sort, $limit, $skip, $unwind, and $group in the correct stage order
Use accumulators $sum, $avg, $min, $max, $push, $addToSet, $first, and $last with multi-field _id grouping keys
Optimize a pipeline by moving $match ahead of $group and using $project to shrink documents early
Write a $lookup in both the localField/foreignField form and the pipeline form using let and a sub-pipeline
Your checkpoint
A timed 30-minute drill producing six aggregation pipelines from written specifications, with all six returning the expected result set.
Milestones
Week 4: Score at least 75% on a 40-question timed mock covering domains 1-4; log every missed item as a topic to revisit in weeks 5-8.
Week 2: Every logged query shows IXSCAN in explain output and you can state in one line why each chosen index shape matches its query.
Week 6: Pass a 15-question transactions quiz at 80% or better and demonstrate a transaction script that both commits and aborts correctly.
Week 8: Score 80% or better on a full-length timed practice exam, with a written error log correcting every missed concept.