C100DEV Practice Test 17 — 15 Questions

Question 1

Refer to the exhibit. An application receives this error during a multi-document transaction. What is the correct approach for the application developer?

Accepted Answer

Use the driver's retry logic to perform the entire transaction again.. The error label 'TransientTransactionError' indicates that the operation failed due to a temporary state, such as a network glitch or a transient deadlock. In MongoDB, these errors are expected and should be handled by the application through a retry mechanism. Using the driver's built-in 'withTransaction' helper function is the recommended practice, as it automatically implements the necessary retry logic for these types of transient, non-permanent failures.

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Terminate the application immediately and alert the database administrator.

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Retry the specific failed write operation only, outside of the transaction.

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Hard-code a try-catch block to skip the transaction and log the error.

Question 2

Which operator is used to perform a left outer join between two collections in an aggregation pipeline?

Accepted Answer

$lookup. The $lookup operator allows for performing an equality match between a field from the input documents and a field from the documents in another collection. It is the primary method for joining data across collections in MongoDB. Mastering $lookup is essential for developers transitioning from relational databases, as it enables the retrieval of related data while keeping the power of the aggregation framework's transformations.

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$join

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$merge

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$unionWith

Question 3

When using the $lookup stage, what happens if the 'foreignField' contains values that are not present in the 'localField' of the joined collection?

Accepted Answer

The result field contains an empty array for those documents.. The $lookup operator acts as a left outer join. If there is no match in the foreign collection for a given document, the resulting array field will simply be empty. This behavior is standard for left outer joins, ensuring that no source documents are dropped from the result set simply because they lack a corresponding record in the joined collection, preserving the integrity of the primary dataset.

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The entire document is dropped from the pipeline result.

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The database throws an error and the aggregation fails.

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The result field is set to null.

Question 4

Which TWO stages are considered 'blocking' stages that can potentially consume excessive memory if not managed correctly?

Accepted Answer

$group. Both $group and $sort are blocking stages because they require all preceding documents to be processed before they can produce their first output document. This behavior means they must maintain state in memory or spill to temporary files on disk if the working set is large. Understanding which stages are blocking is essential for developers to avoid pipeline crashes or performance degradation when handling large-scale data aggregations.

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$match

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$project

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$limit

Question 5

Which aggregation operator is used to transform the shape of documents by adding, renaming, or excluding fields?

Accepted Answer

$project. The $project stage is the primary tool for shaping documents within an aggregation pipeline. It allows developers to specify which fields should be included in the output, calculate new fields, or reshape existing nested documents. Mastering $project is essential for data sanitization, privacy compliance, and preparing output payloads for application consumption by excluding sensitive internal database fields.

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$match

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$group

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$lookup

Question 6

What is the primary benefit of using an index with the $match stage at the beginning of an aggregation pipeline?

Accepted Answer

It enables the engine to quickly select a subset of documents.. When an aggregation pipeline starts with a $match stage, MongoDB can utilize indexes to filter the collection before processing any further stages. This drastically reduces the number of documents loaded into memory, which is the most effective way to optimize pipeline performance. Without index usage, MongoDB must perform a collection scan, which is significantly slower as the dataset grows, making index usage essential for production applications.

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It guarantees that the output documents are sorted.

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It allows the pipeline to bypass the 100MB RAM limit.

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It automatically formats the output fields.

Question 7

Refer to the exhibit. What happens to documents in the collection that do not contain the 'category' field?

Accepted Answer

They are assigned to a group with _id: null.. When grouping by a field, MongoDB treats missing fields or null values as having the value 'null'. Therefore, all documents lacking a 'category' field will be grouped together under the ID 'null'. This behavior is consistent and ensures no documents are silently dropped during a $group operation, allowing developers to identify and handle data quality issues where mandatory fields might be missing from certain records.

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They are excluded from the results.

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The aggregation throws an error.

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They are grouped individually as unique documents.

Question 8

Refer to the exhibit. What is the effect of the $unwind stage on the aggregation pipeline?

Accepted Answer

It flattens the 'details' array, creating one output document per array element.. The $unwind stage deconstructs an array field from the input documents to output a document for each element. By using it after $lookup, it transforms an array of joined data into individual flat documents. This is a critical step when you want to group or sort by fields located inside the joined data, as those operations are much easier to perform on flat documents than on nested array structures.

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It removes the 'details' field entirely from the resulting documents.

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It merges all elements of the 'details' array into a single string.

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It sorts the 'details' array in ascending order.

Question 9

An analytics collection contains order documents with an array of items. You need to output a single document for each element in the items array while preserving documents that have an empty or missing items array. Which stage configuration achieves this?

Accepted Answer

{ $unwind: { path: "$items", preserveNullAndEmptyArrays: true } }. Using the preserveNullAndEmptyArrays option within the $unwind stage ensures that parent documents lacking the target array or containing empty arrays are not dropped from the pipeline result set. This behavior is essential when performing left outer joins or maintaining complete statistical counts across documents with optional nested arrays.

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{ $unwind: "$items" }, { $match: { items: { $exists: false } } }

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{ $project: { items: { $ifNull: ["$items", [null]] } } }, { $unwind: "$items" }

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{ $lookup: { from: "items", localField: "itemId", foreignField: "_id", as: "items", preserveNullAndEmptyArrays: true } }

Question 10

What is the primary function of the explain() method in MongoDB?

Accepted Answer

To provide diagnostic information about query execution.. The explain() method provides detailed information about how a query is executed, including which index was used, the number of documents scanned, and the time taken for the operation. It is the essential diagnostic tool for database administrators to identify performance bottlenecks, verify that queries are using the intended indexes, and optimize slow-running queries by examining the execution plan and resource usage statistics provided by the database engine.

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To automatically optimize the query for faster execution.

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To delete inefficient indexes from the collection.

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To force the query to use a specific index.

Question 11

Which index type should be used to support efficient queries on a field containing geographical coordinates in MongoDB?

Accepted Answer

2dsphere index. The 2dsphere index is specifically designed to support queries on geospatial data using spherical geometry. This index type is essential for location-based applications that perform proximity searches or geometry intersections. By structuring data this way, MongoDB can optimize calculations for distances between points on a sphere, which is crucial for modern mapping and delivery applications that require high precision and performance over large datasets.

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Text index

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Hashed index

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Compound index

Question 12

Refer to the exhibit. Which design pattern is used to store 'total_price' in the document?

Accepted Answer

The Computed pattern.. This is the Computed pattern. By storing the final total in the document, the application avoids recalculating it during every read request. This is particularly valuable in heavy read-heavy applications where the cost of performing arithmetic aggregations across multiple documents is significantly higher than the cost of updating a pre-computed value during the initial write operation. It optimizes read performance at the expense of write complexity.

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The Subset pattern.

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The Polymorphic pattern.

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The Schema Versioning pattern.

Question 13

You are optimizing a query: db.orders.find({ status: 'urgent', amount: { $gt: 500 } }).sort({ order_date: -1 }). According to the ESR (Equality, Sort, Range) rule, what is the ideal index for this query?

Accepted Answer

{ status: 1, order_date: -1, amount: 1 }. The ESR rule is a best practice for ordering fields in a compound index to maximize efficiency. Equality matches should come first to narrow the result set, followed by the Sort field to allow the index to provide the requested order, and finally the Range filter. This order minimizes the number of index keys scanned and avoids in-memory sorts.

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{ amount: 1, order_date: -1, status: 1 }

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{ order_date: -1, status: 1, amount: 1 }

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{ status: 1, amount: 1, order_date: -1 }

Question 14

What happens if a $sort stage exceeds the 100MB RAM limit without an index?

Accepted Answer

The pipeline fails with an error.. If an aggregation stage exceeds the memory limit, the operation will fail unless 'allowDiskUse' is explicitly set to true. When enabled, MongoDB writes the data to temporary files on disk to complete the operation. While this allows large sorts to finish, it significantly degrades performance due to disk I/O, which is orders of magnitude slower than in-memory processing, highlighting the importance of proper indexing.

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It automatically uses disk storage.

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The sort is truncated to 100MB.

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The query planner adds an index.

Question 15

Which operator would you use to filter for documents where an array field contains ALL of the elements in a specified array?

Accepted Answer

$all. The $all operator is the standard solution for matching arrays that contain a set of required values. It is highly effective for filtering documents based on multiple criteria within a single array field, such as tags or categories. Mastery of array query operators is vital for dealing with flexible schema designs, which are a hallmark of MongoDB's document-oriented model.

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$elemMatch

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$in

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$size