MongoDB C100DBA MongoDB Associate Database Administrator(Based on MongoDB 6.0 and 7.0+) Exam Practice Test

For a compound index to satisfy a multi-field sort, the indexed fields must occur in the same sequence as the sort specification, and their directions must either match the requested pattern or be the complete reverse of it. The query sorts by { department: 1, salary: -1 }, so option B exactly matches both the key order and the requested directions. MongoDB can scan that index and return the results already ordered, avoiding a blocking in-memory sort. Option A uses { department: 1, salary: 1 }, which does not match the mixed-direction sort and is not its full inverse. Option D puts name before the two sort keys. Because the query has no equality predicate on name, that leading key prevents the later fields from directly providing the requested global sort. Option C is not a sensible index definition for this workload. Separately, the displayed projection mixes inclusion and exclusion (name:1, salary:1, department:0), which MongoDB normally disallows except for _id; that syntax issue should be corrected independently. It does not change the intended index answer for the sort, which is B.

Study Guide reference/topic:Indexing and Query Optimization - compound indexes, sort support, index prefixes, and key direction.

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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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MongoDB supports direct equality matching against elements contained in an array. When the predicate is { sizes: " M " } and sizes is an array field, a document matches when at least one element of the array equals the scalar value " M " . Document 1 contains only " XS " and " S " , so it does not match. Document 2 contains " M " in [ ' S ' , ' M ' , ' XL ' ], and document 3 contains " M " in [ ' M ' , ' L ' , ' XL ' , ' XXL ' ]; both therefore match. No $elemMatch expression is required for this simple scalar membership test. $elemMatch becomes important when multiple conditions must be satisfied by the same array element, particularly for arrays of embedded documents or compound conditions. This array equality behavior is a core part of MongoDB ' s document query model and allows concise filtering of array-valued fields. The correct result is consequently the t-shirt and vest documents, which corresponds to option D. This same matching behavior applies consistently whenever a scalar predicate targets values stored inside a BSON array field.

Study Guide reference/topic:MongoDB Architecture and Core Operations - querying array fields and equality predicates.

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The documented mongosh helper is db.revokeRolesFromUser(), and its second argument is an array of roles to revoke. MongoDB permits each role in that array to be expressed either as an explicit document { role: < roleName > , db: < database > } or, when the role belongs to the database in which the command is run, as a simple role-name string. Option C uses the explicit role-document form and is therefore valid for a role named specialRole defined in myDB. Option E uses the shorthand string form and is valid when specialRole is in the current database context. Option A reverses the required document structure and does not supply the mandatory role and db keys. Options B and D use a singular helper, db.revokeRoleFromUser(), which is not the documented MongoDB method. The plural method name reflects that multiple roles can be revoked in a single operation. Administrators should also remember that revoking a directly assigned role does not necessarily remove privileges inherited through other roles. Among the choices, C and E are the syntactically valid revocation forms.

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

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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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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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MongoDB custom roles are defined in terms of privileges, and each privilege specifies a resource plus one or more allowed actions. For read/query access, find is a valid privilege action. To represent all normal collections in a specified database, MongoDB uses a resource document with the target database and an empty collection name, for example { db: " products " , collection: " " }. Option B therefore combines the correct resource scope with the find action and does not grant write capability. The distractors confuse built-in role names with privilege actions. read, readWrite, and readAnyDatabase are built-in roles or role concepts, not the appropriate action values for the privileges[].actions list shown here. Likewise, a literal " * " collection value is not the documented database-wide resource form for this custom privilege. In practice, an administrator could also assign MongoDB ' s built-in read role rather than creating a custom role if its permissions exactly match the requirement. Given the specific createRole() choices, however, option B is the technically correct construction.

Study Guide reference/topic:Security, Networking, and Encryption - RBAC, custom roles, resources, and privilege actions.

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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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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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The result depends on how the two concurrent operations interleave at document _id:3. MongoDB guarantees atomicity for each individual document write, but updateMany() is not atomic across the complete set of matching documents. Other operations can run between its per-document modifications. findAndModify also returns the pre-modification version of the selected document by default unless configured to return the updated version. If findAndModify reaches the X-phone first, it observes quantity 850, returns that pre-image, and sets the value to 1250. The concurrent updateMany() can subsequently reach the same document and set it to 0, producing option B. In another valid interleaving, updateMany() first sets the X-phone quantity to 0. findAndModify then selects that document, returns the pre-image showing 0, and sets the value to 1250. Because updateMany() has already processed that document, the final value can remain 1250, producing option D. Options A and C do not correctly reflect the default pre-image return behavior together with the possible per-document interleavings. Therefore B and D are both valid outcomes.

Study Guide reference/topic:MongoDB Architecture and Core Operations - single-document atomicity, concurrent writes, findAndModify, and updateMany.

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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.

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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