C100DBA Practice Exam 2 — 20 Questions

Question 1

Why should you avoid creating too many indexes on a single collection?

Accepted Answer

It increases the cost of write operations.. Every index adds overhead to every write operation (insert, update, delete) because the database must maintain the index structure alongside the collection. Furthermore, indexes consume significant memory, and if the working set (the data and indexes most frequently accessed) exceeds available RAM, the database will experience frequent page faults from disk, leading to severe performance degradation across all types of operations.

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It prevents the use of sharding.

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It automatically disables the WiredTiger cache.

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It forces queries to use a collection scan.

Question 2

Which update operator would you use to increase the value of a numeric field by a specific amount?

Accepted Answer

$inc. The $inc operator is the standard tool for atomic numeric increments. It is highly efficient because it allows the database to perform the math on the server side without requiring the client to read the current value, perform the addition locally, and write it back. This eliminates race conditions where two clients might read the same value and overwrite each other's increments.

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$add

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$set

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$mul

Question 3

Which connection string parameter should be used to ensure that an application only reads data from nodes that are considered current and consistent?

Accepted Answer

readPreference=primary. Read preference settings dictate where the application directs its read operations. Using 'primary' ensures that the application always reads the most recent data, avoiding the latency issues inherent in replication lag. This is critical for applications that require strong consistency and cannot tolerate reading stale data, such as financial transactions or inventory management systems, where data accuracy is paramount.

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readPreference=secondary

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readPreference=nearest

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readPreference=secondaryPreferred

Question 4

Which property of a field makes it a poor candidate for an index?

Accepted Answer

The field has very low cardinality.. Low cardinality fields, such as those with only two or three unique values, provide little benefit when indexed. MongoDB's query optimizer often skips these indexes because a collection scan is usually faster than traversing the B-tree for a large percentage of the dataset. Identifying high-cardinality fields is essential for building effective indexes that genuinely speed up data retrieval rather than just adding overhead to write operations.

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The field is frequently used in filter criteria.

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The field contains unique identifiers like UUIDs.

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The field is a numeric timestamp.

Question 5

Refer to the exhibit. What is the primary benefit of the data structure shown?

Accepted Answer

It allows for indexing the array to support efficient queries.. The exhibit illustrates the use of an array field within a document. By embedding 'tags' directly into the product document, the application can index and query these items without requiring a separate collection or a join. This design effectively demonstrates how the document model supports efficient data retrieval for common filtering patterns, ensuring that the database scales horizontally while keeping related data units cohesive and performant.

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It forces data normalization for better storage efficiency.

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It enforces referential integrity between products and tags.

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It requires a multi-document transaction to update the tags.

Question 6

Refer to the exhibit. A developer attempts to run a query using both 'tags' and 'location' in a single $and operation. What is the expected behavior regarding index usage?

Accepted Answer

The optimizer may choose an index intersection plan.. MongoDB can utilize multiple indexes for a single query through an 'index intersection' execution plan. However, the query optimizer decides whether to perform an intersection or use just one index based on cost. For complex queries involving geo-spatial data and arrays, intersection might be less efficient than a single compound index, and the optimizer may choose a collection scan if intersection costs are deemed too high.

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It will always use both indexes simultaneously.

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It will fail because compound indexes are required.

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It will ignore all indexes and scan the collection.

Question 7

An application requires high write throughput for time-series data indexed by a timestamp field. You observe that all incoming data is being routed to a single shard, creating a write hotspot. Which shard key strategy best resolves this performance bottleneck?

Accepted Answer

Apply a hashed index to the timestamp field as the shard key.. Using a hashed shard key distributes documents uniformly across all shards in the cluster regardless of the timestamp value. By hashing the shard key, the balancer ensures that contiguous ranges of data are not sent to the same shard. This strategy is critical in MongoDB sharding to prevent write hotspots and ensure that the disk I/O and CPU utilization are evenly balanced across the entire sharded cluster, effectively scaling write operations horizontally.

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Use the timestamp field as a single-field shard key.

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Implement a range-based shard key using a UUID.

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Increase the number of mongos instances in the cluster.

Question 8

A company stores IoT sensor data with a high ingestion rate. They use a monotonically increasing timestamp as the shard key. What is the most likely performance bottleneck for this cluster?

Accepted Answer

All new inserts will target the shard holding the highest range of values.. Monotonically increasing shard keys like timestamps result in all write operations being directed to a single shard, specifically the one containing the maximum range. This creates a hot shard where the CPU and I/O capacity of one node are exhausted while others remain idle. Understanding write distribution is critical for maintaining high-throughput ingest systems in MongoDB's distributed architecture.

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The balancer will move chunks too frequently, causing network saturation.

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Read operations will fail because the query router cannot locate the data.

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The shard key must be unique, and timestamps often collide in high-volume systems.

Question 9

What is the purpose of the 'write concern' in MongoDB?

Accepted Answer

To define the durability and replication requirements for a write.. Write concern determines the level of acknowledgement requested from the database for write operations. It is a critical setting for balancing performance against data durability. By choosing the right write concern, developers can ensure that their application meets its specific data safety requirements, whether that means waiting for confirmation from a single node or guaranteeing that the write has been replicated across a majority of the cluster nodes.

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To specify which primary node the write should be directed to.

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To encrypt the data being written to the database.

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To automatically compress the data before it reaches the disk.

Question 10

Which TWO of the following are benefits of the MongoDB 'document' model over the traditional 'relational' model?

Accepted Answer

Built-in support for complex, hierarchical data structures.. The document model excels in developer agility and data modeling flexibility. By grouping related data, it optimizes for common access patterns, which enhances read performance by reducing the need for joins. This approach aligns with modern programming paradigms where objects are the primary unit of development, allowing for faster iterations and easier maintenance compared to relational models that require complex schema changes and manual data normalization across multiple tables.

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Requirement for strict normalization of all data.

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Guaranteed consistency through mandatory schema migrations.

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Automatic translation of SQL queries to BSON.

Question 11

An application frequently queries a large collection by 'user_id' and 'status', sorting the results by 'timestamp'. Which index provides the most efficient execution plan?

Accepted Answer

{ user_id: 1, status: 1, timestamp: 1 }. To optimize this query, the ESR (Equality, Sort, Range) rule must be applied. The 'user_id' and 'status' fields are equality filters, while 'timestamp' is used for sorting. An index of {user_id: 1, status: 1, timestamp: 1} allows MongoDB to satisfy the query filters and provide the sort order directly from the index, avoiding a blocking sort operation in memory which is critical for performance on large collections.

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{ timestamp: 1, user_id: 1, status: 1 }

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{ user_id: 1, timestamp: 1, status: 1 }

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{ user_id: 1, status: 1 }

Question 12

Which method should you use to update multiple documents in a collection and insert a new document if no matches are found?

Accepted Answer

db.collection.updateMany({filter}, {update}, {upsert: true}). The updateMany method modifies all documents matching the filter. When combined with the 'upsert' option set to true, it ensures that if no documents match the query criteria, a single new document is created containing the filter and update criteria. This pattern is critical for idempotent data ingestion pipelines where you must guarantee existence of records without performing separate check-then-insert logic, thus preventing race conditions.

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db.collection.updateOne()

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db.collection.replaceOne()

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db.collection.save()

Question 13

Which read preference allows the application to read from the closest member of the replica set to minimize network latency?

Accepted Answer

nearest. The 'nearest' read preference is specifically designed to reduce latency by targeting the node with the lowest network round-trip time. This is highly effective for global deployments where applications are distributed across regions. Understanding read preferences is fundamental for application administration, as it allows developers to influence traffic distribution and resource utilization across the replica set, ensuring the application remains responsive even when the primary node is geographically distant from the client.

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primaryPreferred

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secondary

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secondaryPreferred

Question 14

When performing a findOne operation, what does MongoDB return if no document matches the query criteria?

Accepted Answer

null. The findOne method is designed to return a single document or null. Returning null is the standard way for the driver and shell to indicate that the search criteria did not produce a match. This is important for application control flow, as developers must always verify that a document was actually retrieved before attempting to access its fields to avoid null pointer exceptions in their code.

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An empty document ({})

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An error code

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An empty cursor

Question 15

What is the purpose of the 'OpLog' in a MongoDB Replica Set?

Accepted Answer

It provides a mechanism for asynchronous data replication.. The OpLog (Operations Log) is a capped collection that records all modifications to the data in the database. It is the core component that allows secondary nodes to replicate data from the primary node. By replaying the operations found in the OpLog, secondaries maintain an identical state to the primary. Understanding the OpLog is essential for diagnosing replication lag and ensuring the system is effectively synchronized across all nodes in the cluster.

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It stores user credentials for database authentication.

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It is used for storing index definitions and metadata.

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It acts as a permanent, immutable backup of all data.

Question 16

Which member type in a MongoDB replica set is best suited for a geographically distant data center to provide read-only access without influencing elections?

Accepted Answer

Priority 0 Secondary. A priority 0 secondary is ideal for this scenario. By setting the priority to zero, the node is ineligible to become primary, preventing it from participating in elections. This is critical for nodes in remote locations where high latency could disrupt election timing or cause unnecessary re-elections, while still providing low-latency read access to users in that specific geographical region.

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Arbiter

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Hidden Member

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Delayed Secondary

Question 17

What is the consequence of having a replica set with only two nodes and no arbiter?

Accepted Answer

The replica set will be unable to elect a primary if one node fails.. A replica set requires a majority of nodes to elect a primary. In a two-node set, a failure of one node makes it impossible to reach a majority, as one out of two is not greater than half. This design limitation is critical because it leads to the loss of a primary and the inability to elect a new one, resulting in a read-only database and complete outage for write operations.

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The set will automatically double the voting power of the primary.

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The replica set will function normally, but writes will be slow.

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The primary will become read-only while the secondary remains idle.

Question 18

Which operator would you use to remove an element from an array field in a document?

Accepted Answer

$pull. The $pull operator is the primary tool for removing items from arrays based on a specified query condition. It is highly effective because it allows you to remove one or many matching elements in a single operation without needing to pull the entire document into application memory, modify the array, and push it back. This promotes efficient resource usage and data consistency.

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$remove

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$pop

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$unset

Question 19

Refer to the exhibit. What is the primary function of the node with _id 2 in this replica set?

Accepted Answer

It provides a vote during elections to help maintain a majority.. An arbiter exists solely to participate in elections and break ties. It does not replicate data or serve read requests, making it a low-resource way to achieve a majority vote in a set. This is crucial for environments where a full-data secondary is cost-prohibitive but high availability is still required to maintain quorum during network partitions.

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It stores a copy of the data and can serve read queries.

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It performs automatic failover by hosting a redundant copy of the oplog.

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It acts as a load balancer for incoming read requests.

Question 20

Refer to the exhibit. Which MongoDB philosophy is represented by this document structure?

Accepted Answer

Data locality for optimized retrieval patterns.. This document structure demonstrates the principle of embedding data. Instead of creating a separate collection for sensor readings and joining them via foreign keys, the readings are nested directly within the document. This approach minimizes the need for multi-table joins, which is a key tenet of MongoDB's design to keep data that is accessed together stored together, resulting in faster read performance and simplified application logic for time-series or hierarchical datasets.

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Normalization for maximum data consistency.

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Enforcement of a rigid schema using array types.

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The use of an Oplog to track document changes.