Latest PDF of C100DEV: MongoDB Certified Developer Associate 2025

MongoDB Certified Developer Associate 2025 Practice Test

C100DEV test Format | Course Contents | Course Outline | test Syllabus | test Objectives

Exam Specification:

- test Name: C100DEV MongoDB Certified Developer Associate
- test Code: C100DEV
- test Duration: 90 minutes
- test Format: Multiple-choice questions
- Passing Score: 65% or higher

Course Outline:

1. Introduction to MongoDB and Data Modeling
- Overview of MongoDB and its key features
- Introduction to NoSQL databases and document-oriented data model
- Designing effective MongoDB data models

2. CRUD Operations and Querying MongoDB
- Performing create, read, update, and delete operations in MongoDB
- Querying data using MongoDB Query Language (MQL)
- Working with indexes and optimizing query performance

3. Aggregation Framework and Data Analysis
- Understanding the MongoDB Aggregation Framework
- Performing data analysis and complex aggregations
- Utilizing pipeline stages, operators, and expressions

4. Data Replication and High Availability
- Configuring replica sets for data replication and high availability
- Managing replica set elections and failover
- Implementing read preference and write concern

5. MongoDB Security and Performance Optimization
- Securing MongoDB deployments using authentication and authorization
- Implementing access controls and user management
- Monitoring and optimizing MongoDB performance

Exam Objectives:

1. Demonstrate knowledge of MongoDB fundamentals, including its data model and key features.
2. Perform CRUD operations and write queries using MongoDB Query Language.
3. Understand and utilize the MongoDB Aggregation Framework for data analysis.
4. Configure and manage MongoDB replica sets for data replication and high availability.
5. Implement MongoDB security measures and optimize performance.

Exam Syllabus:

The test syllabus covers the following courses (but is not limited to):

- MongoDB fundamentals and data modeling
- CRUD operations and querying MongoDB
- Aggregation Framework and data analysis
- Data replication and high availability with replica sets
- MongoDB security and performance optimization

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MongoDB Certified Developer Associate 2025
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and then calls limit.
Question: 269
In a MongoDB application where documents may contain various nested structures, which BSON type would be most suitable for storing data that includes both a list of items and metadata about those items?
rray bject ring
inary Data wer: B
anation: The Object BSON type is suitable for storing complex data tures that include metadata alongside other data types, allowing for a tured representation of nested information.
stion: 270
cenario where you manage "Products," "Orders," and "Customers," w following data modeling choices is likely to create an anti-pattern b
ducing redundancy and complicating the update process for product mation?
A
O
St
B
Ans Expl
struc struc
Que
In a s hich
of the y
intro infor
1. Embedding product details within each order document
2. Storing orders and customers as separate collections with references to products
3. Maintaining a separate "Product" collection linked to orders through product IDs
4. Embedding customer information within order documents for quick access
Answer: A
Explanation: Embedding product details within each order document introduces redundancy, as product information may be repeated for every order. This complicates the update process and increases storage requirements, which is an anti-pattern in data modeling.
MongoDB Python driver, how would you implement an aggregation ine that calculates the average "price" for products grouped by "categ "products" collection?
peline = [{ "$group": { "_id": "$category", "averagePrice": { "$avg": ce" } } }]
peline = [{ "group": { "category": "$category", "avgPrice": { "$avg": ce" } } }]
ollection.aggregate([{ "$group": { "_id": "$category", "avgPrice": { g": "$price" } } }])
peline = [{ "$average": { "$group": { "_id": "$category", "price": "$p
wer: C
anation: The correct syntax for the aggregation pipeline uses $group t egate the results and calculate the average.
Question: 271
In the
pipel ory"
in the
1. pi "$pri
2. pi "$pri
3. c "$av
4. pi rice"
} } }]
Ans
Expl o
aggr
Question: 272
You need to enrich a dataset of users with their corresponding purchase history from another collection. You plan to use the $lookup stage in your aggregation pipeline. What will be the structure of the output documents after the $lookup is executed?
1. Each user document will contain an array of purchase documents that match the user ID.
2. Each purchase document will contain an array of user documents that match the purchase ID.
3. Each user document will contain a single purchase document corresponding to the user ID.
ment. wer: A
anation: The $lookup stage allows you to join documents from one ction into another, resulting in each user document containing an arra hase documents that match the user ID. Option B misrepresents the tion of the join. Option C incorrectly assumes a one-to-one relationsh on D misunderstands how MongoDB handles joined data.
stion: 273
eed to replace an entire document in the inventory collection based o Code. The command you are executing is ventory.replaceOne({itemCode: "A123"}, {itemCode: "A123", itemN
Item", quantity: 50}). What will happen if the document does not ex
new document will be created with the given details.
The output will flatten the user and purchase documents into a single docu
Ans Expl
colle y of
purc
direc ip.
Opti
Que
You n n its
item
db.in ame:
"New ist?
1. A
2. The command will fail because the document must exist to be replaced.
3. The command will succeed, but no changes will be made since the document is missing.
4. The command will log a warning but will not create a new document. Answer: A
Explanation: The replaceOne command with upsert set to true (which is implicit) will create a new document if no document matches the query. However, since upsert is not specified, it will not create a new document in this case.
Question: 274
ations can be performed using the aggregation pipeline in the Mongo driver?
iltering documents based on specific criteria.
rouping documents by a specific field and performing calculations. orting the results of a query based on specified fields.
ll of the above. wer: D
anation: The aggregation pipeline in MongoDB allows for filtering, ping, and sorting of documents, making it a powerful tool for data formation and analysis.
stion: 275
eed to delete a document from the users collection where the userna n_doe". The command you intend to use is db.users.deleteOne({usern
In the context of MongoDB's aggregation framework, which of the following oper DB
1. F
2. G
3. S
4. A
Ans Expl
grou trans
Que
You n me is
"joh ame:
"john_doe"}). What happens if multiple documents match this criteria?
1. All documents with the username "john_doe" will be deleted.
2. Only the first document matching the criteria will be deleted.
3. The command will fail since multiple matches exist.
4. No documents will be deleted, and an error will occur.
Answer: B
Explanation: The deleteOne command removes only the first document that matches the specified filter. Even if multiple documents match, only one will be deleted.
Question: 276
ave a requirement to insert a document into the users collection with ue identifier. The command you execute is db.users.insertOne({userId r001", name: "John Doe"}). If this command is repeated without remo xisting document, which outcome will occur?
he command will succeed, and the existing document will be duplicat he command will fail due to a unique constraint violation on userId. he existing document will be updated with the new name.
he command will throw an error indicating a missing required field. wer: B
anation: If userId is a unique field, attempting to insert a document wi ame userId will result in an error due to the unique constraint violatio enting the insertion.
stion: 277
You h a
uniq :
"use ving
the e
1. T ed.
2. T
3. T
4. T
Ans
Expl th
the s n,
prev
Que
In the MongoDB Go driver, what is the correct syntax for finding a single document in the "employees" collection where the "employeeId" is 12345?
1. collection.FindOne(context.TODO(), bson.M{"employeeId": 12345})
2. collection.FindOne(context.TODO(), bson.D{{"employeeId", 12345}})
3. collection.FindOne(bson.M{"employeeId": 12345})
4. collection.Find(bson.M{"employeeId": 12345}).Limit(1)
Answer: B
Explanation: The FindOne method takes a filter as a parameter, and using bson.D is a common way to construct the filter in the Go driver.
Question: 278
ave a collection called transactions with fields userId, transactionTy reatedAt. A query is scanning through the collection to find all actions of a certain type and then sorts them by createdAt. What inde ld you create to enhance performance?
ransactionType: 1, createdAt: 1 }
reatedAt: 1, userId: 1 }
serId: 1, transactionType: -1 }
ransactionType: -1, createdAt: -1 } wer: A
anation: An index on { transactionType: 1, createdAt: 1 } allows effic ing on transactionType while providing sorted results by createdAt, th ding a collection scan and optimizing query execution time.
stion: 279
You h pe,
and c
trans x
shou
1. { t
2. { c
3. { u
4. { t
Ans
Expl ient
filter us
avoi
Que
In a MongoDB collection where some documents include nested arrays, which query operator would be most effective in retrieving documents based on a specific condition related to the elements of those nested arrays?
1. $unwind
2. $or
3. $not
4. $where Answer: A
stion: 280
When utilizing the MongoDB C# driver, which of the following methods d you employ to bulk insert multiple documents efficiently, taking ntage of the driver's capabilities?
nsertManyAsync() ulkWrite() sertAll() ddRange()
wer: B
anation: The BulkWrite() method is designed for efficiently performi perations, including inserts, updates, and deletes, in a single call, wh
oves performance.
stion: 281
Explanation: The $unwind operator is specifically designed to deconstruct an array field from the input documents to output a document for each element, making it effective for querying nested arrays based on specific conditions.
Que
woul adva
1. I
2. B
3. In
4. A
Ans
Expl ng
bulk o ich
impr
Que
When querying a MongoDB collection where documents may contain an array of sub-documents, which of the following methods or operators would be most effective for retrieving documents based on a condition applied to an element within the array?
1. $elemMatch
2. $type
3. $size Answer: B
cularly effective when dealing with complex data structures that inclu s of sub-documents.
stion: 282
ave a collection named orders that contains documents with fields omerId, amount, and status. You execute the following query: rders.find({ status: 'completed' }).sort({ amount: -1 }).limit(5). Given unt values are non-unique, what will be the expected output format w etrieve the documents?
n array of the top 5 completed orders with the highest amounts, sorte ending order by amount.
n array of all completed orders regardless of amount, sorted in ascend
single document representing the highest completed order only. n empty array if there are no completed orders.
Explanation: The $elemMatch operator allows for precise querying of documents by applying conditions to elements within an array. This is
parti de
array
Que
You h cust
db.o that
amo hen
you r
1. A d in
desc
2. A ing
order.
3. A
4. A
Answer: A
Explanation: The query filters for completed orders, sorts them by amount in descending order, and limits the results to 5 documents, thus returning the top 5 completed orders based on amount.
In a complex aggregation pipeline, you observe that certain stages are significantly slower than others. If you find that a stage is not utilizing an index, which of the following options would be the best initial step to investigate and potentially resolve this performance bottleneck?
1. Increase the size of the aggregation pipeline
ewrite the aggregation pipeline to simplify its stages ncrease the server's hardware resources
wer: B
anation: Using the explain() method provides insights into how the egation stages are executed and whether indexes are being utilized. T mation is crucial for identifying potential issues and optimizing ormance.
stion: 284
usic library application with "Artists," "Albums," and "Tracks," whe artist can produce multiple albums and each album can contain multi s, which of the following data modeling approaches would likely lead ndancy and inefficiencies in retrieving album and track information?
mbedding track details within album documents
Analyze the query with the explain() method to check index usage
2. R
3. I
Ans Expl
aggr his
infor perf
Que
In a m re
each ple
track to
redu
1. E
2. Storing artists and albums in separate collections linked by artist IDs
3. Keeping all entities in a single collection for ease of access
4. Maintaining a separate collection for tracks linked to albums through IDs Answer: C
lead to redundancy and inefficiencies in retrieving album and track information. This anti-pattern complicates data retrieval and can hinder the performance of the application.

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MongoDB Certified Developer Associate 2025 Practice Test

C100DEV test Format | Course Contents | Course Outline | test Syllabus | test Objectives

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C100DEV trial Questions

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