MongoDB C100DBA Dumps

MongoDB read preference controls which replica-set members are eligible to serve read operations. The mode secondary sends reads only to eligible secondary members. If no suitable secondary is available, the read fails rather than falling back to the primary. That strict behavior is exactly what the question asks for. secondaryPreferred, option A, normally routes reads to secondaries but can use the primary when no suitable secondary is available, so it does not guarantee that every read is served by a secondary. Options B and C are not valid MongoDB read preference mode names. Secondary reads are useful for specific workloads, but DBAs must account for asynchronous replication: a secondary can lag the primary, so data returned from a secondary may be stale relative to the latest acknowledged primary write. MongoDB provides additional controls such as tag sets and maximum staleness settings to restrict which secondary members are considered acceptable. When the explicit requirement is to direct all reads away from the primary and exclusively to secondary members, secondary is the correct read preference. Therefore option D is correct.

Study Guide reference/topic:Cluster Reliability and Data Resilience - replica-set read preferences, secondary reads, and staleness.

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db.currentOp() reports information about operations that are currently in progress and accepts a filter document to restrict the output. The ns field in current-operation information identifies the namespace, typically in database.collection form. The regular expression /^qa\./ therefore matches namespaces beginning with qa., which isolates operations targeting collections in the qa database. Option C correctly supplies that namespace expression as a filter argument to db.currentOp(). Options A and B use a nonexistent db.listOps interface. Option D incorrectly treats ns as though it were a property or method chained from db.currentOp; filtering belongs inside the argument document. On current MongoDB versions, administrators are encouraged to use the $currentOp aggregation stage for more powerful filtering and composition, but the legacy helper remains the interface being tested here. Once the problematic operation is identified, the DBA can inspect details such as operation type, namespace, duration, waiting state, and client information before deciding whether additional action such as optimization or termination is justified. Thus C is the correct command.

Study Guide reference/topic:Performance Monitoring and Tuning - active operations, namespaces, long-running operation diagnostics, and currentOp.

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In MongoDB explain output, a COLLSCAN stage means the winning plan is scanning documents in the collection rather than traversing a suitable index. This is the clearest indicator that the query is not being serviced by a normal index scan for the relevant predicate. By contrast, IXSCAN explicitly indicates an index scan and typically includes details such as the index key pattern, direction, and bounds. A FETCH stage represents retrieval of full documents, often after an index has already identified candidate record locations; its presence does not imply that no index was used. GROUP relates to grouping behavior and is not the stage used to identify missing index support. A collection scan is not automatically wrong: for tiny collections or queries that return most documents, it can be efficient. However, on large collections with selective predicates, repeated COLLSCAN plans are a strong signal that the DBA should evaluate indexing. Administrators should compare totalDocsExamined, totalKeysExamined, execution time, and the winning/rejected plans before deciding to add an index. For the terminology tested by the question, COLLSCAN is the correct stage, so C is correct.

Study Guide reference/topic:Indexing and Query Optimization - explain plans, COLLSCAN, IXSCAN, and missing index diagnostics.

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MongoDB supports a clean server shutdown through the shutdown administrative command and through the mongosh helper db.shutdownServer(). Option A uses db.adminCommand({ shutdown: 1 }), which executes the shutdown command against the admin database and requests an orderly termination of the server process. Option D explicitly switches to the admin database and calls db.shutdownServer(), the documented shell helper that wraps the same administrative behavior. Option B uses killOp, which terminates a specific database operation identified by an operation ID; port 27017 is not an operation ID and killing an operation is not equivalent to shutting down mongod. Option C uses killAllSessions, which ends logical sessions but leaves the server process running. A controlled shutdown is preferred over abruptly terminating the process because MongoDB can close resources and complete its shutdown procedures cleanly. In replica-set deployments, administrators should also consider whether the member is primary and whether a planned step-down is appropriate before maintenance. Among the commands shown, A and D are the two valid mongosh-based shutdown mechanisms.

Study Guide reference/topic:MongoDB Architecture and Core Operations - clean server shutdown, admin commands, and db.shutdownServer().

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connections.totalCreated is a cumulative count of incoming connections created during the lifetime of the running MongoDB server process. It includes connections that are still open as well as connections that have already closed. That is why the example can show totalCreated: 5 while current: 4: five connections have been created since the process started, but only four remain current. Option A is incorrect because the counter is not limited to closed connections. Option C corresponds more closely to connections.current, which reports presently existing incoming client connections. Option B is imprecise because “since the server was set up” could imply the lifetime of the installation or deployment across restarts, whereas server-status counters generally reflect the current process lifetime. Tracking totalCreated alongside current, active, and connection-pool behavior helps administrators identify excessive connection churn, poorly configured client pools, or bursts of new connections. The most accurate interpretation among the choices is therefore the number of connections created since the server process was started, making D correct.

Study Guide reference/topic:Performance Monitoring and Tuning - serverStatus, connection metrics, and cumulative connection counters.

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The $in query predicate matches a document when the queried field equals any value listed in the $in array. When the target field is itself an array, MongoDB compares the array elements against the values supplied to $in; one matching element is sufficient. Car 1 qualifies because its features array contains both " Leather Interior " and " 2WD " . Car 3 also qualifies because its array contains " Leather Interior " and " Auto Pilot " . Car 2 contains " Cloth Interior " , " Sun Roof " , and " XM Radio " , none of which are in the predicate array, so it is excluded. A common mistake is to read $in as requiring all values to be present. It does not: the semantics are an OR-style membership test. If the requirement were to ensure that several specified values were all present in an array, a different construct such as $all would be appropriate. Therefore, the result consists of the Toyota Celica and Tesla Model S documents only, making option A correct.

Study Guide reference/topic:MongoDB Architecture and Core Operations - querying documents, arrays, and the $in predicate.

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A replica-set secondary maintains a replicated copy of the primary ' s dataset. The primary records data-changing operations in its oplog, and secondary members continuously copy and apply those oplog entries asynchronously to keep their data synchronized. This redundancy allows an eligible secondary to participate in an election and potentially become primary if the current primary becomes unavailable. mongos, option B, is the query-routing process used in a sharded cluster; it does not store an application dataset copy. An arbiter, option C, participates in replica-set voting and elections but deliberately does not maintain a copy of the database data. A shard, option D, is a data partition in a sharded cluster and is normally implemented as a replica set in production, but “shard” is not the replica-set member role that replicates the primary ' s data. Secondary behavior is central to MongoDB high availability, read-scaling options, backup strategies, and failover. Administrators monitor secondary replication lag and health to ensure the copies remain sufficiently current. Therefore the member responsible for maintaining copies of the primary ' s data is the secondary, making option A correct.

Study Guide reference/topic:Cluster Reliability and Data Resilience - replica-set primaries, secondaries, oplog replication, and redundancy.

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logLevel is a MongoDB server parameter, and server parameters are retrieved through the administrative getParameter database command. The documented and unambiguous mongosh form is db.adminCommand({ getParameter: 1, logLevel: 1 }), which executes the command against the admin database and requests the current value of logLevel. The first numeric value activates the command, while the logLevel field identifies the parameter to return. Options B and C refer to a db.getParameter() helper that is not the documented interface. Option A uses db.runCommand() without explicitly switching to the admin database. Because getParameter is an administrative command, db.adminCommand() is the correct and reliable form among the choices. Administrators use server parameters to inspect and, where supported, tune aspects of mongod or mongos behavior. logLevel controls global diagnostic verbosity, while component-specific verbosity can be managed through the logging configuration. For this question, option D precisely matches the expected administrative syntax. Checking the current value before changing logging settings is also a sound operational practice during troubleshooting.

Study Guide reference/topic:Performance Monitoring and Tuning - runtime server parameters, logging configuration, and getParameter.

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MongoDB equality filters use the form { field: value }. To return the document whose name field contains the exact string " Poppy " , the query therefore uses { name: " Poppy " }, as shown in option D. This shorthand equality syntax is equivalent to explicitly using $eq. Option B uses the correct field but the value is lowercase " poppy " . Normal MongoDB string equality is case-sensitive unless a suitable collation or another case-insensitive technique is deliberately configured, so it does not match the displayed " Poppy " value. Option A uses name.Poppy as a dot-notation path. Dot notation is for accessing a nested field inside an embedded document, but here name stores a scalar string and has no nested Poppy field. Option C searches for a field literally named Poppy, which the sample documents do not contain. This question tests the basic field/value predicate syntax used by find(). The correct command is db.contacts.find({ " name " : " Poppy " }), making option D correct.

Study Guide reference/topic:MongoDB Architecture and Core Operations - find(), equality predicates, string matching, and dot notation.

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MongoDB provides db.grantRolesToUser() specifically for assigning one or more additional roles to an existing user. The method takes the username as its first argument and an array of roles as its second argument, so db.grantRolesToUser( " user1 " , [ " customRole1 " ]) is the correct choice. A role can be supplied as a simple string when it belongs to the current database or as an explicit document such as { role: " customRole1 " , db: " admin " } when the database context must be specified. Option B uses db.createUser(), which is used to create a new user rather than modify the role membership of an account that already exists. Options A and C use method names that are not the documented mongosh interfaces for this operation. Administrators should also distinguish grantRolesToUser() from grantRolesToRole(): the latter makes a user-defined role inherit other roles, whereas the former directly changes a user ' s assigned roles. Least-privilege practice requires granting only roles necessary for the user ' s responsibilities. For the requested update to the existing user, option D is correct.

Study Guide reference/topic:Security, Networking, and Encryption - user administration, role assignment, RBAC, and grantRolesToUser().

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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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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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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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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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MongoDB write concern defines the level of acknowledgment requested for a write. Setting the numeric value w: 1 means that a write is considered acknowledged after the primary has accepted it; no secondary acknowledgment is required. In a replica set with one primary and two secondaries, those secondaries can replicate the operation asynchronously after the client has already received success. That configuration is represented by { writeConcern: { w: 1 } }, making option D correct. The numeric type matters: w can be specified as an integer count or certain strings such as " majority " , but the string " 1 " is not the intended numeric node count shown by the question. Options B and C also omit the w field structure expected inside a write concern document. The durability trade-off is important: a w:1 write can be acknowledged before any secondary has replicated it, so a sudden primary failure before replication may result in that write being rolled back after failover. If the workload requires stronger durability, w: " majority " is commonly used. For exactly one acknowledging node, however, the correct setting is D.

Study Guide reference/topic:Cluster Reliability and Data Resilience - write concern, w acknowledgments, replica-set durability, and rollback risk.

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An upsert combines update behavior with conditional insertion. updateOne() first searches for a document matching { product: " laptop " }. If one exists, $set changes unitsInStock to 25. If none exists and the options document contains { upsert: true }, MongoDB inserts a new document based on the equality portions of the filter together with the update result. Option A uses exactly this pattern. Options B and C incorrectly use insertOne(), which inserts a supplied document and does not accept an update-operator document plus upsert semantics in this form. Option D uses the correct method and update structure but specifies True with a capital T. Mongosh uses JavaScript Boolean syntax, so the literal must be lowercase true. Upserts are useful when an application needs to ensure that a logical entity exists after an operation, but DBAs should consider a unique index on the identifying field when duplicate creation by concurrent requests must be prevented. Given the choices, updateOne(..., {$set:...}, {upsert:true}) is the valid operation, so A is correct.

Study Guide reference/topic:MongoDB Architecture and Core Operations - updateOne(), $set, upserts, and Boolean syntax.

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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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