MongoDB MongoDB Associate Database Administrator(Based on MongoDB 6.0 and 7.0+) C100DBA Exam Dumps: Updated Questions & Answers (October 2026)

A hidden index remains physically present and continues to be maintained as documents are inserted, updated, or deleted, but the query planner does not consider that index when choosing normal execution plans. This makes hidden indexes especially useful for safely testing whether an index can be removed. An administrator can hide the index and observe production query behavior as though the index were absent. If performance or plan quality degrades, the index can be unhidden immediately without rebuilding it because MongoDB has continued to maintain it in the background. Option A is incorrect because a hidden index is still an index and is not simply removed from index statistics. Option B is also incorrect; hiding an index can actually force collection scans or alternative plans if that index was previously important. Option D misunderstands the feature entirely: hiding an index has no effect on document visibility. Hidden indexes are therefore a risk-reduction mechanism for index lifecycle management, especially before dropping an index in a production environment. Option C accurately states that purpose.

Study Guide reference/topic:Indexing and Query Optimization - hidden indexes, query planner behavior, and safe index-removal testing.

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Filesystem snapshots and mongodump are both recognized approaches for backing up self-managed MongoDB deployments, including replica sets. Filesystem snapshots capture the storage volume at a point in time and can be very efficient for large deployments when the underlying storage platform provides consistent snapshot functionality. mongodump creates a logical BSON backup of database contents and supports replica-set-aware options; when writes continue during the dump, oplog capture can be used to support a consistent restore point. The other tools serve different purposes. mongoexport exports collection data to JSON or CSV for interchange or reporting but is not a full-fidelity database backup solution. mongofiles manages content stored in GridFS rather than backing up the database. bsondump reads BSON dump files and renders their contents in a human-readable form; it does not create the original database backup. A sound production strategy also includes restore testing, retention policies, off-site copies, and recovery-point/recovery-time objectives. For the specific choices offered, the two valid backup methods are filesystem snapshots and mongodump, so C and E are correct.

Study Guide reference/topic:Cluster Reliability and Data Resilience - backup strategies, filesystem snapshots, mongodump, and restore planning.

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Sharding is MongoDB ' s horizontal-scaling mechanism: it partitions a collection ' s data across multiple shards so a deployment can scale storage capacity and distribute query or write workload beyond the limits of one server. That directly supports deployments with very large datasets and high-throughput operations, making C correct. MongoDB also supports zone sharding, which associates ranges of shard-key values with selected shards. Zones can be used to keep particular data near a geographical region, satisfy locality requirements, or map data ranges to hardware tiers, so A is also correct. Option B overstates what sharding itself provides; cross-region consistency behavior depends on replication topology, read/write concerns, latency, and application design rather than simply enabling sharding. Option D describes a deployment that already fits comfortably in memory and meets performance goals, so sharding may add unnecessary operational complexity. Option E primarily describes replication: replica sets maintain redundant copies, provide elections, and improve availability. Production shards are themselves normally replica sets, but redundancy is a replication function rather than the defining purpose of sharding. Therefore the two functions represented here are geographic zone distribution and horizontal scale: A and C.

Study Guide reference/topic:Sharding Strategies and Scalable Deployments - horizontal scaling, large datasets, throughput, shard keys, and zone sharding.

MongoDB Enterprise auditing supports three standard destination values for audit events: syslog, file, and console. With the syslog destination, audit records are sent to the operating system ' s syslog facility where supported. With the file destination, events are written to a configured audit log path, and administrators can control the output format according to the supported audit configuration. With the console destination, audit events are emitted to standard output, which can be useful in containerized or centrally collected logging environments. A raw TCP/IP port is not itself an auditLog.destination value. Organizations can certainly forward logs over the network using operating-system or observability tooling, but that transport is external to MongoDB ' s destination setting. A MongoDB database is likewise not one of the supported audit destinations; keeping audit evidence independent from the system being audited is also preferable for integrity and compliance. Auditing records security-relevant actions and can be filtered to meet regulatory or operational requirements. Therefore the three valid destinations among the choices are syslog, file, and console: A, B, and E.

Study Guide reference/topic:Security, Networking, and Encryption - auditing, auditLog.destination, compliance logging, and audit configuration.

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The query filters only on the sizes field, so the most direct supporting index is a single-field index on sizes. Because sizes contains arrays, MongoDB automatically treats the resulting index as a multikey index and creates index entries derived from the array elements. That allows the query { sizes: " M " } to locate matching documents through the index rather than scanning every document in the collection. Option B indexes item, which is not part of the predicate and therefore does not help this query. Option C contains sizes, but _id is the leading key and the query provides no equality restriction on _id; this prevents efficient use of the later key as a standalone prefix for the query. Option D has the same structural problem because item precedes sizes and is not constrained. MongoDB ' s index-prefix rules make a targeted { sizes: 1 } index the strongest choice here. In a real workload, DBAs should also consider write overhead and whether additional queries can share a compound index before creating redundant indexes. For this single query pattern, option A is optimal.

Study Guide reference/topic:Indexing and Query Optimization - multikey indexes, array fields, index prefixes, and query support.

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