Free Practice Questions for MongoDB C100DBA Exam

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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MongoDB ' s flexible document schema is a defining distinction from the conventional fixed-table model associated with relational database systems. Documents in the same MongoDB collection can contain different fields, nested structures, arrays, or representations when the application domain requires that flexibility. MongoDB can still enforce schema rules through validation, so a flexible schema does not mean the absence of governance; it means the physical model is not inherently restricted to one rigid row structure for every record. The other choices are not sufficiently distinguishing. Relational database platforms can provide replication, authentication, and horizontal partitioning or sharding techniques, so those capabilities are not unique architectural differences. MongoDB ' s BSON document model also allows data that is commonly normalized across multiple relational tables to be embedded within one application-oriented document where that design is appropriate. This can reduce joins and map more naturally to object structures used by applications. DBAs must still choose schemas carefully based on access patterns, document growth, duplication, and consistency requirements. Among the choices, Flexible Schema most accurately represents a fundamental MongoDB-versus-RDBMS difference, so option D is correct.

Study Guide reference/topic:MongoDB Architecture and Core Operations - document databases, BSON data modeling, flexible schemas, and relational-model differences.

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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 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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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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Transport Layer Security protects MongoDB network communications while data travels between clients and servers and, when configured, between MongoDB deployment members. Its core purpose is therefore encryption of data in transit. TLS helps prevent passive interception and tampering and also supports certificate-based identity validation. It is separate from encryption at rest, which protects files on storage media, and separate from Queryable Encryption or client-side field encryption, which protects selected application data before it reaches the server. Option A is unrelated: backup and restore operations use snapshot, managed backup, or database-tool mechanisms. Option B describes the objective of replication, elections, and redundant deployment architecture, not TLS. Option C is also incorrect; TLS is a security control, not a query-optimization mechanism, and cryptographic processing can add some overhead rather than improving query execution. A secure MongoDB deployment normally combines TLS with strong authentication, least-privilege authorization, appropriate network exposure controls, auditing, and storage or field-level encryption as required. For the specific question, enabling TLS provides encryption for data moving across the network, making option D correct.

Study Guide reference/topic:Security, Networking, and Encryption - TLS/SSL, transport encryption, certificates, and network security.

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Replica-set elections determine which eligible member serves as primary. An election can occur after the current primary becomes unavailable, after certain reconfigurations, during replica-set initialization, or when a primary is deliberately stepped down. Eligible voting members exchange election information and, when a candidate obtains the required support, that candidate transitions to primary and begins accepting writes. Option A describes asynchronous replication through the oplog, which is a separate mechanism. Option C is unrelated: index creation is an administrative database operation, not a function of elections. Option D is also incorrect because elections do not add members to the replica-set configuration; adding a secondary requires an explicit membership change such as rs.add() or equivalent configuration management. Elections are the foundation of automatic failover and are one reason MongoDB replica sets can provide high availability without a human manually designating a replacement primary after every failure. During an election there can be a brief period in which writes are unavailable, so applications should use retryable behavior and appropriate driver topology handling. The purpose described in option B is therefore correct.

Study Guide reference/topic:Cluster Reliability and Data Resilience - replica-set elections, primary selection, voting, and automatic failover.

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