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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Section	Objectives
Data Modelling	- Scalable Data Models 1. Understand Liquid Clustering versus partitioning and Z-Ordering 2. Optimize data layout using Liquid Clustering 3. Design and implement scalable data models using Delta Lake - Dimensional Modelling 1. Design dimensional models for analytical workloads
Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Build append-only pipelines for batch and streaming data using Delta 2. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data 3. Ingest data from message buses and cloud storage
Monitoring and Alerting	- Monitoring 1. Use Lakeflow Spark Declarative Pipelines event logs for monitoring 2. Use system tables for resource, cost, audit, and workload monitoring 3. Use Databricks REST APIs and CLI for monitoring jobs and pipelines 4. Use Query Profiler and Spark UI to monitor workloads - Alerting 1. Configure Lakeflow Jobs notifications for job status and performance issues 2. Use SQL Alerts for data quality monitoring
Data Transformation, Cleansing, and Quality	- Advanced Data Transformation 1. Apply window functions, joins, and aggregations to large datasets 2. Write efficient Spark SQL and PySpark transformations - Data Quality 1. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader 2. Develop data quarantining processes for invalid data
Cost & Performance Optimisation	- Delta Optimization 1. Understand deletion vectors and liquid clustering 2. Apply data skipping and file pruning techniques 3. Use Change Data Feed to address streaming table limitations and improve latency - Query Performance 1. Use Query Profile to identify performance bottlenecks 2. Identify inefficient joins and excessive data shuffling - Cost Optimization 1. Understand how Unity Catalog managed tables reduce operational overhead
Ensuring Data Security and Compliance	- Data Security 1. Apply anonymization and pseudonymization techniques 2. Use row filters and column masks for sensitive data 3. Use ACLs to secure workspace objects and enforce least privilege - Compliance 1. Implement pipelines that detect and mask personally identifiable information 2. Develop data purging solutions according to data retention policies
Data Governance	- Metadata and Discoverability 1. Create and maintain descriptions and metadata for enterprise data - Unity Catalog Permissions 1. Understand the Unity Catalog permission inheritance model
Developing Code for Data Processing using Python and SQL	- Building and Testing ETL Pipelines 1. Use control flow operators in pipeline components 2. Compare streaming tables and materialized views 3. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 4. Configure environments, dependencies, memory, and retry behavior 5. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader 6. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 7. Develop unit and integration tests for data processing code 8. Use APPLY CHANGES APIs for change data capture - Using Python and Tools for Development 1. Manage and troubleshoot third-party library installations and dependencies 2. Develop User-Defined Functions using Pandas/Python UDFs 3. Design and implement scalable Python project structures optimized for Databricks Asset Bundles
Data Sharing and Federation	- Delta Sharing 1. Configure Databricks-to-Databricks Sharing 2. Configure sharing with external platforms using the open sharing protocol 3. Share live Lakehouse data with external computing platforms - Lakehouse Federation 1. Configure Lakehouse Federation with appropriate governance
Debugging and Deploying	- Debugging and Troubleshooting 1. Analyze errors and remediate failed job runs 2. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging 3. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics - Deploying CI/CD 1. Integrate Git-based CI/CD workflows using Databricks Git Folders 2. Build and deploy Databricks resources using Databricks Asset Bundles

>> Certified-Data-Engineer-Professional인증덤프문제 <<

Certified-Data-Engineer-Professional최신 시험 최신 덤프, Certified-Data-Engineer-Professional최신 인증시험자료

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최신 Databricks Certification Certified-Data-Engineer-Professional 무료샘플문제 (Q208-Q213):

질문 # 208
A senior data engineer is planning large-scale data workflows. The current task is to identify the considerations that form a foundation for creating scalable data models that are essential for effective management of large datasets. The data engineering team has identified the core capabilities as part of a scalable data model to build a modern data platform and provided their reasoning for considering Delta Lake for review. The senior data engineer is responsible for identifying the recommendations that are not valid. Which key features can be ignored while evaluating Delta Lake?

A. Delta Lake optimizes metadata handling, efficiently managing billions of files and facilitating scalability to petabyte-scale datasets.
B. Delta Lake provides limited support for monitoring and troubleshooting data pipelines, so relevant partner tools have to be identified and set up for enhanced operational efficiency.
C. Delta Lake's capability to process data in both batch and streaming modes seamlessly, providing flexibility in data ingestion and processing.
D. Delta Lake works with various data formats (e.g., Parquet, JSON, CSV) and integrates well with Spark and Databricks tools.

정답：B

설명：
Delta Lake includes built-in capabilities for monitoring, auditing, and troubleshooting through transaction logs, history, and tight integration with the Databricks platform. Therefore, limited support for monitoring and troubleshooting is not a valid concern when evaluating Delta Lake and can be ignored.

질문 # 209
A data engineer needs to design an efficient pipeline that automatically processes new CSV files as they arrive in S3 storage. Which Databricks feature should the data engineer use to meet these requirements?

A. Traditional batch processing with scheduled Databricks Jobs
B. Auto Loader with schema inference and evolution enabled
C. COPY INTO SQL command with parameters to track processed files
D. Streaming from cloud storage using standard Spark readStream with format ("csv") and format ("json")

정답：B

설명：
Auto Loader is designed to efficiently and incrementally process new files as they arrive in cloud object storage. It provides scalable file discovery, supports schema inference and evolution, and minimizes overhead compared to traditional batch or manual streaming approaches.

질문 # 210
A data engineer is attempting to execute the following PySpark code:
df = spark.read.table("sales")
result = df.groupBy("region").agg(sum("revenue"))
However, upon inspecting the execution plan and profiling the Spark job, they observe excessive data shuffling during the aggregation phase.
Which technique should be applied to reduce shuffling during the groupBy aggregation operation?

A. Use coalesce() after the aggregation.
B. Use broadcast join.
C. Caching the DataFrame df.
D. Repartition by region before aggregation.

정답：D

설명：
Repartitioning the DataFrame by the grouping key ensures that records with the same region are colocated in the same partitions before the aggregation runs. This significantly reduces the amount of data shuffled during the groupBy operation, leading to more efficient execution.

질문 # 211
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

A. Improves the quality of your data
B. Validates a complete use case of your application
C. Ensures that all steps interact correctly to achieve the desired end result
D. Yields faster deployment and execution times
E. Troubleshooting is easier since all steps are isolated and tested individually

정답：E

설명：
Unit tests are small, isolated tests that are used to check specific parts of the code, such as functions or classes.

질문 # 212
A data engineer is analyzing transactional data in a PySpark DataFrame df containing customer_id, transaction_timestamp (precise to milliseconds), and amount_spent. The objective is to compute a cumulative sum of amount_spent per customer, strictly ordered by transaction_timestamp. The cumulative sum must include all transactions from the earliest timestamp up to and including the current row, respecting temporal ordering within each customer partition. Which PySpark code snippet most accurately constructs the appropriate window specification and applies the aggregation to yield the correct cumulative expenditure per customer?

정답：A

설명：
This window specification partitions the data by customer_id, orders transactions by transaction_timestamp, and defines the frame from the first transaction through the current one.
This guarantees that the cumulative sum is computed independently per customer and strictly follows the temporal order, including all prior transactions up to the current row.

질문 # 213
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