Exam Code: DP-300
Exam Name: Azure Database Administrator Associate
Passing Score: 70%
Duration: 100 Minute
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65 Questions
65 Questions
65 Questions
65 Questions
Select the appropriate service for a computer vision solution
Select the appropriate service for a natural language processing solution
Select the appropriate service for a speech solution
Select the appropriate service for a generative AI solution
Select the appropriate service for a document intelligence solution
Select the appropriate service for a knowledge mining solution
Plan for a solution that meets Responsible AI principles
Create an Azure AI resource
Determine a default endpoint for a service
Integrate Azure AI services into a continuous integration and continuous delivery (CI/CD) pipeline
Plan and implement a container deployment
Configure diagnostic logging
Monitor an Azure AI resource
Manage costs for Azure AI services
Manage account keys
Protect account keys by using Azure Key Vault
Manage authentication for an Azure AI Service resource
Manage private communications
Implement a text moderation solution with Azure AI Content Safety
Implement an image moderation solution with Azure AI Content Safety
Select visual features to meet image processing requirements
Detect objects in images and generate image tags
Include image analysis features in an image processing request
Interpret image processing responses
Extract text from images using Azure AI Vision
Convert handwritten text using Azure AI Vision
Choose between image classification and object detection models
Label images
Train a custom image model, including image classification and object detection
Evaluate custom vision model metrics
Publish a custom vision model
Consume a custom vision model
Use Azure AI Video Indexer to extract insights from a video or live stream
Use Azure AI Vision Spatial Analysis to detect presence and movement of people in video
Extract key phrases
Extract entities
Determine sentiment of text
Detect the language used in text
Detect personally identifiable information (PII) in text
Implement text-to-speech
Implement speech-to-text
Improve text-to-speech by using Speech Synthesis Markup Language (SSML)
Implement custom speech solutions
Implement intent recognition
Implement keyword recognition
Translate text and documents by using the Azure AI Translator service
Implement custom translation, including training, improving, and publishing a custom model
Translate speech-to-speech by using the Azure AI Speech service
Translate speech-to-text by using the Azure AI Speech service
Translate to multiple languages simultaneously
Create intents and add utterances
Create entities
Train, evaluate, deploy, and test a language understanding model
Optimize a language understanding model
Consume a language model from a client application
Backup and recover language understanding models
Create a question answering project
Add question-and-answer pairs manually
Import sources
Train and test a knowledge base
Publish a knowledge base
Create a multi-turn conversation
Add alternate phrasing
Add chit-chat to a knowledge base
Export a knowledge base
Create a multi-language question answering solution
Provision an Azure AI Search resource
Create data sources
Create an index
Define a skillset
Implement custom skills and include them in a skillset
Create and run an indexer
Query an index, including syntax, sorting, filtering, and wildcards
Manage Knowledge Store projections, including file, object, and table projections
Provision a Document Intelligence resource
Use prebuilt models to extract data from documents
Implement a custom document intelligence model
Train, test, and publish a custom document intelligence model
Create a composed document intelligence model
Implement a document intelligence model as a custom Azure AI Search skill
Provision an Azure OpenAI Service resource
Select and deploy an Azure OpenAI model
Submit prompts to generate natural language
Submit prompts to generate code
Use the DALL-E model to generate images
Use Azure OpenAI APIs to submit prompts and receive responses
Configure parameters to control generative behavior
Apply prompt engineering techniques to improve responses
Use your own data with an Azure OpenAI model
Fine-tune an Azure OpenAI model
Select the appropriate service for a computer vision solution
Select the appropriate service for a natural language processing solution
Select the appropriate service for a speech solution
Select the appropriate service for a generative AI solution
Select the appropriate service for a document intelligence solution
Select the appropriate service for a knowledge mining solution
Plan for a solution that meets Responsible AI principles
Create an Azure AI resource
Determine a default endpoint for a service
Integrate Azure AI services into a continuous integration and continuous delivery (CI/CD) pipeline
Plan and implement a container deployment
Configure diagnostic logging
Monitor an Azure AI resource
Manage costs for Azure AI services
Manage account keys
Protect account keys by using Azure Key Vault
Manage authentication for an Azure AI Service resource
Manage private communications
Implement a text moderation solution with Azure AI Content Safety
Implement an image moderation solution with Azure AI Content Safety
Select visual features to meet image processing requirements
Detect objects in images and generate image tags
Include image analysis features in an image processing request
Interpret image processing responses
Extract text from images using Azure AI Vision
Convert handwritten text using Azure AI Vision
Choose between image classification and object detection models
Label images
Train a custom image model, including image classification and object detection
Evaluate custom vision model metrics
Publish a custom vision model
Consume a custom vision model
Use Azure AI Video Indexer to extract insights from a video or live stream
Use Azure AI Vision Spatial Analysis to detect presence and movement of people in video
Extract key phrases
Extract entities
Determine sentiment of text
Detect the language used in text
Detect personally identifiable information (PII) in text
Implement text-to-speech
Implement speech-to-text
Improve text-to-speech by using Speech Synthesis Markup Language (SSML)
Implement custom speech solutions
Implement intent recognition
Implement keyword recognition
Translate text and documents by using the Azure AI Translator service
Implement custom translation, including training, improving, and publishing a custom model
Translate speech-to-speech by using the Azure AI Speech service
Translate speech-to-text by using the Azure AI Speech service
Translate to multiple languages simultaneously
Create intents and add utterances
Create entities
Train, evaluate, deploy, and test a language understanding model
Optimize a language understanding model
Consume a language model from a client application
Backup and recover language understanding models
Create a question answering project
Add question-and-answer pairs manually
Import sources
Train and test a knowledge base
Publish a knowledge base
Create a multi-turn conversation
Add alternate phrasing
Add chit-chat to a knowledge base
Export a knowledge base
Create a multi-language question answering solution
Provision an Azure AI Search resource
Create data sources
Create an index
Define a skillset
Implement custom skills and include them in a skillset
Create and run an indexer
Query an index, including syntax, sorting, filtering, and wildcards
Manage Knowledge Store projections, including file, object, and table projections
Provision a Document Intelligence resource
Use prebuilt models to extract data from documents
Implement a custom document intelligence model
Train, test, and publish a custom document intelligence model
Create a composed document intelligence model
Implement a document intelligence model as a custom Azure AI Search skill
Provision an Azure OpenAI Service resource
Select and deploy an Azure OpenAI model
Submit prompts to generate natural language
Submit prompts to generate code
Use the DALL-E model to generate images
Use Azure OpenAI APIs to submit prompts and receive responses
Configure parameters to control generative behavior
Apply prompt engineering techniques to improve responses
Use your own data with an Azure OpenAI model
Fine-tune an Azure OpenAI model
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10 reviews for Azure Database Administrator Associate
Upasna
March 15, 2026 at 01:49 pm
Studying for DP-300 (Azure Database Administrator Associate) with AllITPrep made Azure database tasks much easier to follow on Microsoft. The material helped me review important admin concepts like performance, security, and monitoring. I felt confident during the exam and passed with an 77% score.
ragini
February 22, 2026 at 04:47 pm
Getting ready for DP-300 with AllITPrep felt calm and well-paced. Instead of heavy theory, the learning focused on understanding how Azure databases are monitored, secured, and maintained in real use. The clear flow and simple practice helped me stay confident and in control on exam day.
nirman
December 16, 2025 at 10:43 am
The DP-300 Azure Database Administrator Associate certification makes learning Microsoft Azure databases simple and easy. It focuses on managing, monitoring, and keeping databases running smoothly, aiming for a 72% passing score. AllITPrep makes exam prep clear, simple, and beginner-friendly.
Arvind Rao
February 16, 2025 at 07:37 pm
Thanks to ALL ITprep, I passed DP-300 on my first attempt. Their dumps are top-notch!
Tanisha Gupta
February 04, 2025 at 07:37 pm
I passed DP-300 with ease, thanks to ALL ITprep. The content was highly relevant, and I felt well-prepared for the exam.