C100DBA Practice Test 3 — 10 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.