Práctica para el exámen | MS Azure DP-203 Ingeniería de dato

Prepare for Your Azure Data Engineer Exam: Practice Questions & Key Skills

This resource provides valuable practice questions to help you prepare for your Azure Data Engineer certification exam. Important: These questions are not the official exam questions, but they cover the core material outlined in the knowledge sections. The official exam requirements are regularly updated, so this practice material is kept current.

What You'll Find Here:

• Realistic Scenario-Based Questions: Many questions present real-world scenarios, testing your ability to apply data engineering concepts.
• English & Spanish Translations: Understanding both English and Spanish terminology used in Microsoft services is key. We provide translations to help you recognize components used within Azure.
• Detailed Explanations & Resources: Each question includes a thorough explanation of the correct answer and links to reference materials for deeper understanding.
• Dynamic Question Sets: Questions are randomized each time you retake the test, ensuring you focus on understanding the why behind the answers, not just memorizing correct choices.

Disclaimer: This course should not be your sole study resource. It's designed to complement your existing learning materials. If you spot any content needing attention, please send a screenshot. Note that question numbers are unique to each test instance and won't help identify specific questions.

Core Competencies for Azure Data Engineers:

As an Azure Data Engineer, you'll be responsible for integrating, transforming, and consolidating data from diverse sources (structured, unstructured, streaming) into a suitable schema for analytics. You'll empower stakeholders through data exploration and build/maintain secure, reliable data processing pipelines using various Azure tools and techniques. This includes designing data stores using various architectural patterns like:

• Data Warehouse (MDW)
• Big Data
• Lakehouse Architecture

You'll also ensure high performance, efficiency, organization, and reliability of data pipelines and data stores, addressing operational and data quality issues. Key responsibilities include designing, implementing, monitoring, and optimizing data platforms to meet data pipeline demands.

Essential Skills:

A strong understanding of data processing languages is crucial:

• SQL
• Python
• Scala

You should also be familiar with data architecture patterns and parallel processing. Proficiency in the following Azure services is expected for building data processing solutions:

• Azure Data Factory
• Azure Synapse Analytics
• Azure Stream Analytics
• Azure Event Hubs
• Azure Data Lake Storage
• Azure Databricks
• Azure Data Brick

Skill Breakdown (Approximate Weighting):

• Data Storage Design & Implementation (15-20%):
  • Implement partitioning strategies for files.
  • Implement partitioning strategies for analytical workloads.
  • Implement partitioning strategies for streaming workloads.
  • Implement partitioning strategies in Azure Synapse Analytics.
  • Determine when partitioning is necessary in Azure Data Lake Storage Gen2.
  • Design and implement a data exploration layer.
• Data Processing Development (40-45%):
  • Ingest and transform data.
  • Design and implement incremental loads.
  • Transform data using Apache Spark.
  • Transform data using Transact-SQL (T-SQL) in Azure Synapse Analytics.
  • Ingest and transform data using Azure Synapse Pipelines or Azure Data Factory.
  • Transform data using Azure Stream Analytics.
  • Data Cleaning: Handle duplicates, missing data, and late-arriving data.
  • Data splitting, JSON parsing, encoding/decoding.
  • Error handling, data normalization/denormalization, and exploratory data analysis.
  • Develop batch processing solutions using Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data Factory.
  • Utilize PolyBase for data loading into SQL Pools.
  • Implement Azure Synapse Link for querying replicated data.
  • Create data pipelines: scaling resources, configuring batch sizes, and integrating Jupyter notebooks/Python.
• Data Storage & Processing Security, Monitoring, & Optimization (30-35%):
  • Implement data security measures: data masking, encryption at rest and in transit, row/column-level security, Azure RBAC, POSIX ACLs (Data Lake Storage Gen2), data retention policies, secure endpoints (private/public), and resource tokens (Azure Databricks).
  • Manage sensitive information.
  • Implement data storage and processing monitoring using Azure Monitor.
  • Configure monitoring services, track streaming processing, measure data movement performance, and monitor pipeline performance.