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Certification

Syllabus

mongo_certification-syllabus.png

MongoDB overview

  • Understand Database, Collections and Documents
  • MongoDB stores data records as BSON documents
  • BSON is a binary representation of JSON documents

Sample Scenario: Given a set of documents, identify what documents can co-exist in a collection

CRUD

Insert Methods:

  • db.collection.insertOne()
  • db.collection.insertMany()
  • db.collection.bulkWrite()

Identify properly formed insert statements

Read Method:

  • db.collection.find(query, projection, options)
  • Use of Comparison, Logical, Element and Array query and projection operators
  • Common curson methods: sort, limit0), count, and skip()

Sample Scenario: Identify projection in a query like: db.product.find( { type: "A" ), { item: 1, color: 1 })

Update Methods:

  • db.collection.updateOne(<filter>, <update>, <options>)
  • db.collection.updateMany(<filter>, <update>, <options>)
  • db.collection.replaceOne(<filter>, <update>, <options>)

Additional Update Methods:

  • db.collection.updateOne(<filter>, <update>, <options>)
  • db.collection.findOneAndReplace()
  • db.collection.findOneAndUpdate()
  • db.collection.findAndModify()

Update Operators: $set, $inc, Sunset, $max

Array Update Operators and Upsert

Delete Methods:

  • db.collection.deleteMany()
  • db.collection.deleteOne()
  • findOneAndDelete()
  • db.collection.remove()

Build query filters

Sample Scenario: Identify a delete expression to remove a given document from a collection

Aggregation:

Aggregation pipeline consists of one or more stages that process documents

Create an aggregation pipeline using db.collection.aggregate() method

Build an aggregation pipeline using $match, $group, $sort, $project, etc

Sample Scenario: Identify the output of an aggregation expression

Indexes

Why use Indexes in MongoDB?

Types of Indexes offered by MongoDB

Methods to manage indexes: - db.collection.createIndex() - db.collection.getIndexes() - db.collection.dropIndex()

Limitations of using Indexes

Sample Scenario: Understand the Explain result and information on query plans and execution statistics of the query plans

Data Modeling

Flexible Schema

Data Model Design: - Embedded Data Models - Normalized Data Models

Tools and Tooling

Atlas data explorers feature

Driver

  • Purpose of Driver and it's advantages
  • Connect and Understand components of URI String
  • CRUD syntax for chosen Driver
  • Aggregation syntax for chosen Driver

Sample scenario will be same as CRUD

Tip

https://learn.mongodb.com/learn/course/mongodb-associate-developer-exam-study-guide/main/associate-dba-exam-study-guide

Cursor, toArray

tokenization {  "mappings": {    "dynamic": false,    "fields": {         "name": [   {  "type": "autocomplete",                              "tokenization": "edgeGram"} ]    } }}

tokenization algorithms

MongoDB atlas search db.restaurants.aggregate([{    "$search": {      "text": { "path": "name", "query": "cuban"}    } }])

Atlas search index

All aggregation pipelines

getSiblingDB

upsert and other options

All CRUD operations, what every query return keep eye on that also

index in reverse, effects of index on search and sort, indexes on array

Pooling in nodejs, mongoclient

Syntax

Schema types and designs