Pass the MongoDB Certified DBA Associate C100DBA Questions and answers with Dumpstech

When MongoDB ' s winning query plan uses a B-tree index, the plan includes an IXSCAN stage. That stage contains the index-specific information a DBA needs to identify how the query is being serviced, including the key pattern, index name, traversal direction, and index bounds. For example, keyPattern can show a compound definition such as { status: 1, createdAt: -1 }, making it straightforward to confirm which indexed keys participate in the chosen plan. A FETCH stage indicates that MongoDB is retrieving full documents, often after an index has produced candidate record identifiers; the fetch stage itself does not identify the index key. A COLLSCAN stage means MongoDB is scanning the collection and therefore is not using a normal index scan for the predicate. GROUP is unrelated to index selection. Effective DBA query analysis usually involves inspecting both queryPlanner.winningPlan and execution statistics such as totalKeysExamined, totalDocsExamined, and nReturned. But for the narrow question of which stage exposes the index key used by the winning plan, IXSCAN is the correct answer. Therefore option C is correct.

Study Guide reference/topic:Indexing and Query Optimization - explain output, winning plans, IXSCAN, key patterns, and index bounds.

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db.collection.stats() returns statistics for a specific collection, including storage-related values that help a DBA understand that collection ' s disk footprint. Therefore, for a collection named order, db.order.stats() is the relevant command among the choices. The result can include metrics such as logical data size, allocated storage size, document count, index information, and related collection statistics depending on version and storage engine. Option A, db.stats(), reports database-level statistics aggregated across the database rather than isolating the order collection. db.hostInfo(), option C, reports host and operating-system information rather than collection storage consumption. db.serverStatus(), option D, provides broad server-process runtime metrics such as connections, memory, operation counters, replication/storage-engine information, and other health data, but it is not the collection-specific disk-utilization interface requested here. Administrators can also use dedicated helpers such as collection storage-size or total-size methods when they need a more narrowly defined measurement. For a general collection-level statistics query targeting order, however, db.order.stats() is the correct answer, making option B correct.

Study Guide reference/topic:Performance Monitoring and Tuning - collection statistics, storage utilization, db.collection.stats(), and database/server metrics.

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The first argument to find() is the query filter and the second argument is the projection. Because the requirement applies to every employee, the filter must be an empty document {}. The projection must include name and age while explicitly suppressing _id. MongoDB includes _id by default even in inclusion projections, so { name: 1, age: 1 } would return three fields: _id, name, and age. Adding _id: 0 is the documented exception that allows _id to be excluded from an otherwise inclusion-style projection. Option C therefore produces exactly the requested fields. Options A and B mistakenly place projection-like expressions in the filter position, which would cause MongoDB to search for documents where those fields literally match the specified values rather than controlling returned fields. Option D is close but violates the word “ONLY” because _id remains present. Projection design can also reduce network traffic and, when a suitable index covers both the predicate and projected fields, can contribute to covered-query plans. For this requirement, however, the essential syntax is find({}, {name:1, age:1, _id:0}), so C is correct.

Study Guide reference/topic:MongoDB Architecture and Core Operations - find(), filters, projections, inclusion fields, and _id exclusion.

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mongosh, the MongoDB Shell, is the interactive command-line environment used to connect to MongoDB deployments and perform administrative and data operations. Through mongosh, a DBA can run CRUD queries, aggregation pipelines, user and role administration, replica-set commands, server diagnostics, index operations, and many other management tasks. mongod, option B, is the MongoDB database server process itself; it stores data and serves requests but is not the interactive administration client. mongostat, option C, is a monitoring utility that reports a concise stream of server activity metrics such as operation rates, memory, connections, and queue information. mongotop, option D, reports time spent reading and writing data by namespace and is likewise a diagnostic utility rather than a general administration shell. DBAs may also use MongoDB Compass, Atlas interfaces, Ops Manager, or automation tooling depending on the deployment, but among these four command-line tools, mongosh is the one intended for broad interactive access and administration. Therefore option A is correct.

Study Guide reference/topic:MongoDB Architecture and Core Operations - MongoDB Shell, administration tools, server processes, and monitoring utilities.

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Adding replica-set members addresses availability and recovery objectives. A normal secondary maintains a replicated copy of the primary ' s data and can be elected primary if the current primary becomes unavailable, thereby improving service availability compared with a single-node deployment. That directly supports option D. A delayed member intentionally applies oplog entries later than the primary, preserving an older logical state of the dataset for a configured delay period. This can help recover from operator mistakes such as an accidental destructive update or drop operation because the administrator may be able to access the delayed copy before the damaging operation is applied there. That supports option A. A delayed member is not a substitute for long-term archival or a formal backup-retention policy; its recoverable history is constrained by the delay configuration and oplog window, so B is not the intended objective. Option C describes sharding, which partitions data across shards rather than simply replicating it. Encryption at rest is configured independently and is unrelated to adding secondaries or delayed members. Therefore, A and D are the two reasons that align with the proposed replication design.

Study Guide reference/topic:Cluster Reliability and Data Resilience - replica sets, delayed members, failover, and operator-error recovery.

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updateMany() distinguishes between documents that match the filter and documents whose stored data actually changes. The filter { diagnosis: " Hypertension " } matches Carlos Martinez and Maria Lopez, so matchedCount is 2. Carlos currently has room: 203, and $set: { room: 207 } changes his document, contributing one to modifiedCount. Maria already has room: 207. She still satisfies the filter and therefore counts as a match, but setting a field to the value it already contains does not produce a material modification, so she does not increment modifiedCount. Juan Perez has diagnosis Arrhythmia, so he is neither matched nor modified. The resulting acknowledged operation therefore reports matchedCount: 2 and modifiedCount: 1, corresponding to option D. This distinction is essential when validating whether an update actually changed stored values rather than merely identifying matching records. This distinction is operationally important: a large difference between matched and modified counts can indicate that many matching documents already contained the requested state, not that the update failed. DBAs should use both counters when validating bulk-update behavior.

Study Guide reference/topic:MongoDB Architecture and Core Operations - updateMany(), $set, matchedCount, and modifiedCount.

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