Latest PDF of C100DEV: MongoDB Certified Developer Associate 2024

MongoDB Certified Developer Associate 2024 Practice Test

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

Exam Specification:

- Exam Name: C100DEV MongoDB Certified Developer Associate
- Exam Code: C100DEV
- Exam Duration: 90 minutes
- Exam 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 exam syllabus covers the following topics (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
C100DEV
MongoDB Certified Developer Associate 2024
https://killexams.com/pass4sure/exam-detail/C1000EV
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?
A. Array
B. Object
C. String
D. Binary Data
Answer: B
Explanation: The Object BSON type is suitable for storing complex data
structures that include metadata alongside other data types, allowing for a
structured representation of nested information.
Question: 270
In a scenario where you manage "Products," "Orders," and "Customers," which
of the following data modeling choices is likely to create an anti-pattern by
introducing redundancy and complicating the update process for product
information?
A. Embedding product details within each order document
B. Storing orders and customers as separate collections with references to
products
C. Maintaining a separate "Product" collection linked to orders through product
IDs
D. 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.
Question: 271
In the MongoDB Python driver, how would you implement an aggregation
pipeline that calculates the average "price" for products grouped by "category"
in the "products" collection?
A. pipeline = [{ "$group": { "_id": "$category", "averagePrice": { "$avg":
"$price" } } }]
B. pipeline = [{ "group": { "category": "$category", "avgPrice": { "$avg":
"$price" } } }]
C. collection.aggregate([{ "$group": { "_id": "$category", "avgPrice": {
"$avg": "$price" } } }])
D. pipeline = [{ "$average": { "$group": { "_id": "$category", "price": "$price"
} } }]
Answer: C
Explanation: The correct syntax for the aggregation pipeline uses $group to
aggregate the results and calculate the average.
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?
A. Each user document will contain an array of purchase documents that match
the user ID.
B. Each purchase document will contain an array of user documents that match
the purchase ID.
C. Each user document will contain a single purchase document corresponding
to the user ID.
D. The output will flatten the user and purchase documents into a single
document.
Answer: A
Explanation: The $lookup stage allows you to join documents from one
collection into another, resulting in each user document containing an array of
purchase documents that match the user ID. Option B misrepresents the
direction of the join. Option C incorrectly assumes a one-to-one relationship.
Option D misunderstands how MongoDB handles joined data.
Question: 273
You need to replace an entire document in the inventory collection based on its
itemCode. The command you are executing is
db.inventory.replaceOne({itemCode: "A123"}, {itemCode: "A123", itemName:
"New Item", quantity: 50}). What will happen if the document does not exist?
A. A new document will be created with the given details.
B. The command will fail because the document must exist to be replaced.
C. The command will succeed, but no changes will be made since the
document is missing.
D. 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
In the context of MongoDB's aggregation framework, which of the following
operations can be performed using the aggregation pipeline in the MongoDB
driver?
A. Filtering documents based on specific criteria.
B. Grouping documents by a specific field and performing calculations.
C. Sorting the results of a query based on specified fields.
D. All of the above.
Answer: D
Explanation: The aggregation pipeline in MongoDB allows for filtering,
grouping, and sorting of documents, making it a powerful tool for data
transformation and analysis.
Question: 275
You need to delete a document from the users collection where the username is
"john_doe". The command you intend to use is db.users.deleteOne({username:
"john_doe"}). What happens if multiple documents match this criteria?
A. All documents with the username "john_doe" will be deleted.
B. Only the first document matching the criteria will be deleted.
C. The command will fail since multiple matches exist.
D. 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
You have a requirement to insert a document into the users collection with a
unique identifier. The command you execute is db.users.insertOne({userId:
"user001", name: "John Doe"}). If this command is repeated without removing
the existing document, which outcome will occur?
A. The command will succeed, and the existing document will be duplicated.
B. The command will fail due to a unique constraint violation on userId.
C. The existing document will be updated with the new name.
D. The command will throw an error indicating a missing required field.
Answer: B
Explanation: If userId is a unique field, attempting to insert a document with
the same userId will result in an error due to the unique constraint violation,
preventing the insertion.
Question: 277
In the MongoDB Go driver, what is the correct syntax for finding a single
document in the "employees" collection where the "employeeId" is 12345?
A. collection.FindOne(context.TODO(), bson.M{"employeeId": 12345})
B. collection.FindOne(context.TODO(), bson.D{{"employeeId", 12345}})
C. collection.FindOne(bson.M{"employeeId": 12345})
D. 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
You have a collection called transactions with fields userId, transactionType,
and createdAt. A query is scanning through the collection to find all
transactions of a certain type and then sorts them by createdAt. What index
should you create to enhance performance?
A. { transactionType: 1, createdAt: 1 }
B. { createdAt: 1, userId: 1 }
C. { userId: 1, transactionType: -1 }
D. { transactionType: -1, createdAt: -1 }
Answer: A
Explanation: An index on { transactionType: 1, createdAt: 1 } allows efficient
filtering on transactionType while providing sorted results by createdAt, thus
avoiding a collection scan and optimizing query execution time.
Question: 279
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?
A. $unwind
B. $or
C. $not
D. $where
Answer: A
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.
Question: 280
When utilizing the MongoDB C# driver, which of the following methods
would you employ to bulk insert multiple documents efficiently, taking
advantage of the driver's capabilities?
A. InsertManyAsync()
B. BulkWrite()
C. InsertAll()
D. AddRange()
Answer: B
Explanation: The BulkWrite() method is designed for efficiently performing
bulk operations, including inserts, updates, and deletes, in a single call, which
improves performance.
Question: 281
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?
A. $exists
B. $elemMatch
C. $type
D. $size
Answer: B
Explanation: The $elemMatch operator allows for precise querying of
documents by applying conditions to elements within an array. This is
particularly effective when dealing with complex data structures that include
arrays of sub-documents.
Question: 282
You have a collection named orders that contains documents with fields
customerId, amount, and status. You execute the following query:
db.orders.find({ status: 'completed' }).sort({ amount: -1 }).limit(5). Given that
amount values are non-unique, what will be the expected output format when
you retrieve the documents?
A. An array of the top 5 completed orders with the highest amounts, sorted in
descending order by amount.
B. An array of all completed orders regardless of amount, sorted in ascending
order.
C. A single document representing the highest completed order only.
D. An empty array if there are no completed orders.
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.
Question: 283
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?
A. Increase the size of the aggregation pipeline
B. Analyze the query with the explain() method to check index usage
C. Rewrite the aggregation pipeline to simplify its stages
D. Increase the server's hardware resources
Answer: B
Explanation: Using the explain() method provides insights into how the
aggregation stages are executed and whether indexes are being utilized. This
information is crucial for identifying potential issues and optimizing
performance.
Question: 284
In a music library application with "Artists," "Albums," and "Tracks," where
each artist can produce multiple albums and each album can contain multiple
tracks, which of the following data modeling approaches would likely lead to
redundancy and inefficiencies in retrieving album and track information?
A. Embedding track details within album documents
B. Storing artists and albums in separate collections linked by artist IDs
C. Keeping all entities in a single collection for ease of access
D. Maintaining a separate collection for tracks linked to albums through IDs
Answer: C
Explanation: Keeping all entities in a single collection for ease of access can
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 2024 Practice Test

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