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Exam Number : C1000DEV
Exam Name : MongoDB Certified Developer Associate
Vendor Name : MongoDB
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C1000DEV Exam Format | C1000DEV Course Contents | C1000DEV Course Outline | C1000DEV Exam Syllabus | C1000DEV Exam Objectives

Exam Specification:

- Exam Name: C1000DEV MongoDB Certified Developer Associate
- Exam Code: C1000DEV
- 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 Study Guide


Amazon’s code-generating device receives MongoDB-particular enhancements | Resources

Between open supply and cloud-hosted, extra proprietary options, there’s an abundance of AI-powered code-producing tools to choose between. So how does one choose? That’s a nuanced query. beyond stronger performance on specific units of programming languages or logic complications, there’s no longer a lot to distinguish one code-producing device from an additional.

but Amazon’s looking to exchange that.

these days, the business introduced that its code-generating device accessible via AWS, Amazon CodeWhisperer, has been “optimized” to supply “stronger” counsel for app construction on MongoDB, the open source database administration application. Now, CodeWhisperer can supply superior MongoDB-related code techniques that mirror top of the line practices, Amazon says — enabling developers to prototype extra straight away.

“regardless of what utility a developer is building, they could now get generative AI-powered code suggestions that adhere to MongoDB most beneficial practices,” Deepak Singh, VP of subsequent-gen developer event at AWS, informed TechCrunch by means of electronic mail. “Our joint valued clientele in specific will now benefit from optimized information across each AWS and MongoDB, additional accelerating construction when building incredibly scalable, cloud-based mostly purposes.”

To increase CodeWhisperer’s suggestions bearing on MongoDB, AWS says that it worked with the group protecting the database — MongoDB the business — to coach CodeWhisperer’s underlying AI mannequin on “tremendously curated” content and code across C#, Go, Java, JavaScript and Python. The working towards data got here from MongoDB documentation, use instances and common MongoDB tasks like records aggregations and operations), and the MongoDB team took lively half in evaluating CodeWhisperer’s output right through the practising procedure.

“training AI-powered coding equipment is an iterative system, and we’re excited in regards to the outcomes we’ve seen to this point,” Andrew Davidson, SVP of product at MongoDB, advised TechCrunch in an electronic mail interview. “We’ll continue working with the Amazon CodeWhisperer group to extra optimize performance and accuracy to deliver a fair better experience for developers building purposes with MongoDB Atlas on AWS.”

Amazon and MongoDB have a long historical past collectively, having collaborated on the launch of MongoDB Atlas, a completely managed MongoDB provider on AWS, round seven years ago. So the CodeWhisperer collaboration doesn’t come as an entire shock. but I’m curious to peer what form of precedent it establishes within the industrial code era area.

for example, will we are expecting AWS to work with other companies on codebased-selected optimizations to CodeWhisperer sooner or later? if so, will cash be worried — and did funds alternate fingers between MongoDB and Amazon for this optimization, for that count? AWS and MongoDB deny it.

Then, there’s the talents legal ramifications to agree with. Microsoft, GitHub and OpenAI are presently being sued in a class action motion that accuses them of violating copyright legislation via enabling Copilot, GitHub’s own code-generating device, to regurgitate licensed code snippets devoid of providing credit score. Amazon, perhaps, is laying the groundwork to stay away from the identical destiny.

in any case, i ponder whether competitors like GitHub will kind their personal relationships with vendors to respond to Amazon’s CodeWhisperer improvements. Amazon obviously sees the MongoDB tie-in as a unique selling factor — and complementary to its a few different MongoDB products.

I’d are expecting not anytime quickly — if ever. Copilot, while reportedly a cash loser, is capable of energy consumer-base-smart, with smartly over a million paying individual clients and greater than 37,000 enterprise shoppers. Then once more, stranger issues have took place in the generative AI area — lawsuit-driven, competitors-spurred or in any other case.


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