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| Section | Objectives |
|---|---|
| Topic 1: Application Administration | - Security and Access Control
|
| Topic 2: CRUD Operations | - Data Manipulation
|
| Topic 3: Philosophy and Features | - MongoDB Architecture
|
| Topic 4: Replication | - Replica Sets
|
| Topic 5: Server Administration | - Database Operations
|
| Topic 6: Sharding | - Cluster Scaling
|
| Topic 7: Indexes | - Index Management
|
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NEW QUESTION # 110
A MongoDB collection named users contains sample documents with fields including name, age, address, description, city, and occupation.
The administrator uses the following query to perform a collection scan:
db.users.find({ city: " New York " , occupation: " Software Engineer " }) What compound index will improve the performance of the query the MOST?
Answer: A,C
Explanation:
The query contains equality predicates on both city and occupation, so an efficient compound index should contain both fields. Options A and C include only one query predicate plus the unrelated name field, forcing MongoDB to perform additional filtering that the index cannot resolve. Options B and D both contain the complete predicate set. For a query consisting entirely of equality predicates, MongoDB does not require those equality keys to appear in a particular order relative to one another for the query itself; { city: 1, occupation: 1 } and { occupation: 1, city: 1 } can both support the two equality conditions effectively. The original question appears to expect B because it mirrors the field order used in the query, but that distinction is not technically required under MongoDB ' s equality-key guidance. Field order becomes much more significant when the workload also needs a particular index prefix, sort order, range predicate, or when one ordering better supports additional common queries. Accordingly, the technically accurate review result is B and D, with B being the likely intended exam choice.
Study Guide reference/topic:Indexing and Query Optimization - compound indexes, equality predicates, and index key ordering.
NEW QUESTION # 111
In a replica set, which member is responsible for maintaining copies of the primary ' s data?
Answer: B
Explanation:
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.
NEW QUESTION # 112
In a collection that contains 100 post documents, what does the following command do? db. posts.
find().skip(5).limit(5)
Answer: A
NEW QUESTION # 113
Which type of indexes does MongoDB support?
Answer: C
NEW QUESTION # 114
Consider the following documents:
You perform the following query;
How many documents will be updated by the query?
Answer: A
NEW QUESTION # 115
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