DP-900 Practice Exam

用户

Create a practice exam for the MS DP-900.

ConciseGPT

MS DP-900 Practice Exam

Data Workloads:
- Define "relational data."
- What is "non-relational data"?
- Describe "big data."
Azure Data Services:
- List two features of Azure SQL Database.
- Explain Azure Cosmos DB's consistency models.
- Name an advantage of Azure Synapse Analytics.
Data Storage:
- Differentiate between structured and unstructured data.
- How does data lake storage work?
- What is a data warehouse?
Data Processing:
- Describe a data pipeline.
- What is stream processing?
- Give an example of batch processing.
Data Security and Compliance:
- Explain GDPR.
- What is role-based access control?
- Define data encryption in transit.

Answer all questions. Time: 90 minutes.

**MS DP-900 Practice Exam** 1. **Data Workloads:** - Define "relational data." - What is "non-relational data"? - Describe "big data." 2. **Azure Data Services:** - List two features of Azure SQL Database. - Explain Azure Cosmos DB's consistency models. - Name an advantage of Azure Synapse Analytics. 3. **Data Storage:** - Differentiate between structured and unstructured data. - How does data lake storage work? - What is a data warehouse? 4. **Data Processing:** - Describe a data pipeline. - What is stream processing? - Give an example of batch processing. 5. **Data Security and Compliance:** - Explain GDPR. - What is role-based access control? - Define data encryption in transit. Answer all questions. Time: 90 minutes.

用户

Make it multiple choice.

ConciseGPT

MS DP-900 Practice Exam (Multiple Choice)

Data Workloads:
- Define "relational data": a) Data in key-value pairs b) Data in tables with relationships c) Unstructured data d) Large datasets
- What is "non-relational data"? a) Data in SQL format b) Data in tables without fixed schema c) Data in Excel files d) Data in structured query language
- Describe "big data": a) Large volumes of structured data b) Large volumes of unstructured data c) Large datasets with complex patterns d) All of the above
Azure Data Services:
- Two features of Azure SQL Database: a) High availability and JSON support b) NoSQL support and machine learning c) Data lake integration and XML support d) In-memory computing and graph database
- Azure Cosmos DB's consistency models include: a) Eventual and strong consistency b) Immediate and delayed consistency c) Local and global consistency d) Sequential and parallel consistency
- An advantage of Azure Synapse Analytics: a) Real-time analytics b) Automatic data cleaning c) Inbuilt AI capabilities d) Native graph processing
Data Storage:
- Structured vs. unstructured data: a) Structured is more flexible b) Unstructured has a pre-defined model c) Structured is in tabular form d) Unstructured cannot be stored digitally
- Data lake storage: a) Stores structured data only b) Optimized for large files c) Primarily for transactional data d) Same as traditional databases
- What is a data warehouse? a) For real-time data processing b) Optimized for writing data c) Designed for analytical processing d) Only for unstructured data
Data Processing:
- A data pipeline: a) Is a static data model b) Moves and transforms data c) Processes data in real-time only d) Is used only for small datasets
- Stream processing: a) Handles data in batches b) Processes data as it arrives c) Is less efficient than batch processing d) Only for audio and video streams
- Example of batch processing: a) Sensor data analysis b) E-commerce transactions c) Nightly inventory updates d) Social media feeds
Data Security and Compliance:
- GDPR is about: a) Global data placement rules b) European data protection c) General data processing regulations d) Government data privacy regulations
- Role-based access control: a) Assigns permissions based on job function b) Encrypts data at rest c) Monitors network traffic d) Is a firewall feature
- Data encryption in transit: a) Encrypts data being moved b) Encrypts data being deleted c) Stores data in encrypted format d) Encrypts only emails

Answer all questions. Time: 90 minutes.

**MS DP-900 Practice Exam (Multiple Choice)** 1. **Data Workloads:** - Define "relational data": a) Data in key-value pairs b) Data in tables with relationships c) Unstructured data d) Large datasets - What is "non-relational data"? a) Data in SQL format b) Data in tables without fixed schema c) Data in Excel files d) Data in structured query language - Describe "big data": a) Large volumes of structured data b) Large volumes of unstructured data c) Large datasets with complex patterns d) All of the above 2. **Azure Data Services:** - Two features of Azure SQL Database: a) High availability and JSON support b) NoSQL support and machine learning c) Data lake integration and XML support d) In-memory computing and graph database - Azure Cosmos DB's consistency models include: a) Eventual and strong consistency b) Immediate and delayed consistency c) Local and global consistency d) Sequential and parallel consistency - An advantage of Azure Synapse Analytics: a) Real-time analytics b) Automatic data cleaning c) Inbuilt AI capabilities d) Native graph processing 3. **Data Storage:** - Structured vs. unstructured data: a) Structured is more flexible b) Unstructured has a pre-defined model c) Structured is in tabular form d) Unstructured cannot be stored digitally - Data lake storage: a) Stores structured data only b) Optimized for large files c) Primarily for transactional data d) Same as traditional databases - What is a data warehouse? a) For real-time data processing b) Optimized for writing data c) Designed for analytical processing d) Only for unstructured data 4. **Data Processing:** - A data pipeline: a) Is a static data model b) Moves and transforms data c) Processes data in real-time only d) Is used only for small datasets - Stream processing: a) Handles data in batches b) Processes data as it arrives c) Is less efficient than batch processing d) Only for audio and video streams - Example of batch processing: a) Sensor data analysis b) E-commerce transactions c) Nightly inventory updates d) Social media feeds 5. **Data Security and Compliance:** - GDPR is about: a) Global data placement rules b) European data protection c) General data processing regulations d) Government data privacy regulations - Role-based access control: a) Assigns permissions based on job function b) Encrypts data at rest c) Monitors network traffic d) Is a firewall feature - Data encryption in transit: a) Encrypts data being moved b) Encrypts data being deleted c) Stores data in encrypted format d) Encrypts only emails Answer all questions. Time: 90 minutes.