C100DBA Exam Dumps - MongoDB Certified DBA Associate Questions and Answers

The outer $or returns a document if either branch is true. Document 1 satisfies the first branch because product equals " x-small box " , so it is included regardless of the second branch. The nested $and requires both qty != 50 and the absence of " brown " from the colors array. Document 2 has quantity 60 but contains " brown " , so it fails $nin. Document 3 has quantity 45 and its colors do not include brown, so it satisfies both conditions. Document 4 has no brown but quantity is exactly 50, so it fails $ne: 50. Document 5 has quantity 10 and colors [ ' grey ' , ' white ' ], so it also satisfies both nested predicates. The final set is therefore documents 1, 3, and 5. This question also illustrates that MongoDB applies an implicit AND between multiple top-level field predicates in the same document, but here the explicit $and makes the required conjunction clear. Option D is the only answer containing exactly the matching documents.

Study Guide reference/topic:MongoDB Architecture and Core Operations - logical query operators, $or, $and, $ne, and $nin.

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db.collection.insertOne() inserts one BSON document into a collection. In mongosh, string values must be quoted and Boolean literals use JavaScript syntax: lowercase true or false. Therefore the student ' s name must appear as " Randall " , and the passed flag must be true. Option D is the only choice that gets both value types correct. Option A quotes the string properly but uses True, which is not the JavaScript Boolean literal. Options B and C leave Randall unquoted, so mongosh would interpret it as an identifier or variable rather than as the literal string required by the document. Option C additionally uses the invalid capitalized Boolean form. If an _id field is omitted, MongoDB automatically adds a unique _id, typically an ObjectId, before storing the document. That behavior does not affect the two requested fields. This question tests both CRUD syntax and the BSON/JavaScript literal representation used by mongosh. The correct command is db.class101.insertOne({ student: " Randall " , passed: true }), so option D is correct.

Study Guide reference/topic:MongoDB Architecture and Core Operations - insertOne(), BSON values, strings, Booleans, and automatic _id.

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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 supported mongosh helpers for replica-set membership changes are rs.remove() and rs.add(). rs.remove(hostname) removes the member whose host string matches the supplied host:port, while rs.add() adds a new member using either a host string or a member configuration document that includes a host field. Option C uses those documented methods in the required sequence: remove node 1, remove node 2, then add node 1 back. The names rs.removeNode() and rs.addNode() shown in the other options are not standard MongoDB replica-set helper methods. Membership changes must be performed carefully because modifying voting or data-bearing membership can affect majority calculations, elections, and availability. Administrators normally execute membership changes while connected to the current primary, allow configuration propagation, and verify replica-set health afterward with rs.status() or related diagnostics. If a removed data-bearing node is later re-added, it may need to synchronize depending on how far its data has diverged from the current replica-set history. Among the choices, option C is the only valid command sequence.

Study Guide reference/topic:Cluster Reliability and Data Resilience - replica-set membership, rs.remove(), rs.add(), and topology changes.

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db.collection.deleteOne() deletes a single document that matches the supplied filter, so the direct operation is db.scientificPapers.deleteOne({ doi: " 10.1000/xyz123 " }). The DOI is the identifying criterion, and no additional filter is required. Option A is dangerous and incorrect: drop() removes the entire collection and its indexes rather than deleting a filtered document. Option B uses findOneAndDelete(), which can delete a single matching document, but the displayed filter also requires a nonexistent or inappropriate field/value pair, scientificPapers: " orders " , so the target document would not match. Option D misunderstands the method signature. The second argument to deleteOne() is an options document for settings such as write concern, collation, or hint; it is not a second query predicate. In production, a unique index on DOI could also ensure that the business identifier is unique and make the deletion highly selective. Among the presented commands, only option C cleanly and correctly deletes the one scientific-paper document identified by the specified DOI.

Study Guide reference/topic:MongoDB Architecture and Core Operations - delete operations, deleteOne(), filters, and collection management.

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The most direct evidence that storage capacity is approaching exhaustion is high filesystem utilization and explicit operating-system errors showing that no space remains. Sustained disk utilization above 90% is a practical warning threshold because the deployment has little headroom for database growth, index builds, temporary files, journal activity, or operational spikes. The errno:28 No space left on device message is even stronger evidence: the operating system rejected a write because the relevant filesystem could not allocate additional space. The other choices can correlate with workload growth but do not specifically prove a capacity problem. Query execution time can increase because of poor indexes, cache misses, lock or ticket contention, or CPU pressure. A rising document count says nothing about remaining free space without knowing document size, index size, and volume capacity. Low read/write ticket availability reflects storage-engine concurrency pressure rather than filesystem capacity. Administrators should therefore monitor disk-used and disk-free metrics, alert well before full utilization, and investigate any ENOSPC-style log events immediately. B and D are the two storage-capacity indicators.

Study Guide reference/topic:Performance Monitoring and Tuning - disk capacity, storage metrics, alerts, and diagnostic logs.

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