MongoDB Associate Certification Training (Developer and DBA)
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M1 — The Document Model and BSON Types
BSON types beyond JSON: ObjectId, Decimal128, date and binary values
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Embedded against referenced documents and the 16 MB document ceiling
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Collection design: what happens when one document grows without bound
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Schema validation with $jsonSchema and how strictness affects the write path
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Working from mongosh and from the Python and Node.js drivers
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Choosing _id values that keep write locality and index size sane
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M2 — CRUD, Filters and the Query Language
find and findOne with filters, projection, sort and limit pushed to the server
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Comparison, logical and element operators on nested fields
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Array operators including $elemMatch, $all and $size inside a filter
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Update operators: $set, $inc, $push, $pull and the pipeline form of update
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Delete safety: narrowing the filter before you run a destructive write
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Reading explain("executionStats") and judging the winning plan
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M3 — Aggregation Pipelines and Data Shaping
Core stages in order: $match, $project, $group, $unwind and $sort
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$lookup against a second collection and what a large join costs
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$facet, $bucket and $sample for multi-faceted report blocks in one pass
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Window functions with $setWindowFields for running totals and ranks
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Writing results back with $merge and $out and the difference between them
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Optimising a pipeline by matching early, trimming $project and using indexes
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M4 — Indexing and Query Performance
Single field, compound and multikey indexes and the prefix rule
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Text, wildcard and hashed indexes and the query each one answers
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Partial and sparse indexes over a subset of a large collection
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Covered queries, index bounds and the hidden sort stage in the plan
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Index hints, index intersection and overriding a bad planner choice
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Index upkeep: TTL indexes, background builds and the storage they cost
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M5 — Write Concern, Read Preference and Transactions
Write concern levels and the guarantee each one gives an acknowledged write
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Read preference modes: primary, secondary, nearest and preference tags
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Retryable writes and reads and the conditions that stop them retrying
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Multi-document transactions and why a large one is expensive
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Schema choices that keep writes local and documents from growing forever
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Sizing the oplog window against replica set recovery time
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M6 — Replication, the Oplog and Backup
Replica set members, election terms and the vote that picks a primary
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Reading the oplog and the write concern that keeps it durable
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Backing up with mongodump and mongorestore and the logical dump trade-off
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Resyncing from the oplog, hidden members and delayed replication
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Watching lag, oplog rollover and disk pressure across set members
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Testing a point-in-time restore before the day you need it
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M7 — Sharding, Atlas and Search
Shard key choice: cardinality, frequency and write distribution
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Chunk splitting, the balancer and how a range skew slows the whole set
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Config servers, mongos routers and the path of a query to its shard
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Scatter-gather cost and when targeted routing beats a broadcast
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Atlas tiers, dedicated clusters, serverless and private network peering
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Atlas Search: building the index, the pipeline stage and the analyser
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M8 — Capstone: Model, Aggregate and Operate a MongoDB Service
Design a document model for an orders and events domain and defend it
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Build the collections, validators and indexes for the stated query patterns
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Write the aggregation pipeline that produces the monthly revenue mart
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Stand up a replica set, then shard the collection with a chosen shard key
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Restore the data set to a new cluster and measure the recovery point
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Write the exam revision plan and answer a full Associate practice paper
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