Rating: 3.9(6 ratings) Students: 195 students Language: English Instructor: Kim Rosé | AWS Certified Cloud Practitioner, Microsoft Certified
This practice test is designed to help you succeed by simulating the actual exam experience. Whether you're a beginner or an experienced professional, this course offers the ideal platform for you to strengthen your understanding of data engineering concepts using Microsoft Fabric.
What This Course Offers:
This practice test contains expert-level questions that cover all the key topics tested on the DP-700 exam. By taking this test, you'll get a clear picture of what to expect on exam day. With realistic, high-quality questions that closely resemble the official exam, you’ll be able to assess your knowledge and ensure you're fully prepared.
The DP-700 exam focuses on implementing data engineering solutions using Microsoft Fabric, and this practice test covers every aspect of that, from data loading and orchestration to optimization and security. Each question is carefully crafted to provide you with a challenging yet fair assessment of your skills.
Practice Test 1: Core Data Engineering Concepts
Topics Covered:
Data Ingestion and Streaming: Advanced methods for data ingestion and processing streaming data.
Cosmos DB Integration: Query tuning, indexing strategies, and multi-region replication.
Query Optimization: Performance tuning and execution plan analysis for complex queries.
Security and Compliance: Implementing encryption, auditing, and policy management.
Delta Lake: Time travel, schema evolution, and transaction management.
Data Storage and Analytics: Best practices for scalable and secure data storage.
Synapse Spark Pools: Managing and optimizing Spark workloads in Synapse.
Real-Time Data Processing: Processing and analyzing real-time data streams.
ETL and Data Transformation: Advanced ETL pipeline creation and optimization.
Data Partitioning: Strategies for improving query performance and scalability.
Pipeline Monitoring: Tools and techniques for diagnosing and optimizing pipeline performance.
Access Control: Managing roles, permissions, and security policies.
Practice Test 2: Advanced Data Engineering with Microsoft Fabric
Topics Covered:
Data Orchestration: Automating workflows with Microsoft Fabric's orchestration tools.
Real-Time Analytics: Advanced techniques for gaining insights from real-time data.
Data Security and Secrets Management: Implementing secure data handling and managing secrets.
Performance Optimization: Enhancing the performance of data pipelines and queries.
Azure Networking: Configuring private endpoints, VNETs, and secure data flow.
Cosmos DB Integration: Advanced indexing, partitioning, and consistency settings.
Delta Lake: Building robust solutions with ACID transactions and data versioning.
Data Factory Integration: Creating sophisticated ETL pipelines using Azure Data Factory.
Monitoring and Diagnostics: Leveraging diagnostic tools for pipeline and resource monitoring.
Data Lakehouse: Designing and managing hybrid storage and analytical solutions.
Practice Test 3: Scalable and Cost-Effective Solutions
Topics Covered:
Data Storage and Analytics: Strategies for managing large-scale data and optimizing queries.
Data Loading: Techniques for high-performance batch and incremental loading.
Synapse Link: Using Synapse Link for real-time integration with operational databases.
Data Modeling and Schema Management: Designing efficient schemas for analytics and storage.
Cost Management: Balancing cost with performance for large-scale workloads.
Pipeline Development and Monitoring: Advanced troubleshooting and optimization techniques.
Data Management and Governance: Implementing policies for compliance and data integrity.
Real-Time Data Processing: Handling low-latency requirements for real-time data pipelines.
Data Partitioning: Techniques for distributing data across nodes for optimal performance.
Performance Monitoring: Proactive resource and query performance management.
Practice Test 4: Comprehensive Exam Simulation
Topics Covered:
Data Integration and Transformation: Implementing complex ETL processes.
Real-Time Analytics: Building real-time dashboards and reporting systems.
Security and Compliance: Enterprise-level implementation of security and compliance practices.
Synapse Spark Pools and Delta Lake: Advanced Spark and Delta Lake configurations.
Data Exploration and Query Optimization: Writing and tuning advanced queries.
Data Factory Integration: Managing multi-source data pipelines with Data Factory.
Cost Optimization: Techniques for reducing costs in data engineering solutions.
Monitoring and Diagnostics: Comprehensive diagnostic strategies for large-scale deployments.
Data Storage and Analytics: Managing data lakes, warehouses, and hybrid solutions.
Data Orchestration and Governance: Automating processes while maintaining compliance.
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