Certified-Data-Engineer-Professional試験の準備方法｜更新するCertified-Data-Engineer-Professional関連資格試験対応試験｜便利なDatabricks Certified Data Engineer Professional最新知識

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

Section	Objectives
Debugging and Deploying	- Deploying CI/CD 1. Configure and integrate Git-based CI/CD workflows using Databricks Git folders for notebook and code deployment 2. Build and deploy Databricks resources using Databricks Asset Bundles - Debugging and Troubleshooting 1. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors 2. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines 3. Analyze errors and remediate failed job runs using job repairs and parameter overrides
Data Governance	- Govern enterprise data 1. Create and add descriptions and metadata to enterprise data to improve discoverability 2. Demonstrate understanding of the Unity Catalog permission inheritance model
Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Create an append-only data pipeline capable of handling both batch and streaming data using Delta 2. Ingest formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, text, and binary data from sources such as message buses and cloud storage
Data Modeling	- Design and optimize data models 1. Design dimensional models for analytical workloads with efficient querying and aggregation 2. Identify the benefits of liquid clustering over partitioning and Z-Ordering 3. Simplify data layout decisions and optimize query performance using liquid clustering 4. Design and implement scalable data models using Delta Lake to manage large datasets
Cost & Performance Optimization	- Optimize cost and performance 1. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling 2. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden 3. Understand Delta optimization techniques such as deletion vectors and liquid clustering 4. Apply Change Data Feed to address streaming table limitations and improve latency 5. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning
Ensuring Data Security and Compliance	- Ensuring Compliance 1. Develop data purging solutions that comply with data retention policies 2. Implement compliant batch and streaming pipelines that detect and mask PII - Applying Data Security Mechanisms 1. Use row filters and column masks to protect sensitive table data 2. Use ACLs to secure workspace objects and enforce the principle of least privilege 3. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization
Data Transformation, Cleansing, and Quality	- Transform and validate data 1. Write efficient Spark SQL and PySpark code for advanced transformations including window functions, joins, and aggregations 2. Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or Auto Loader in classic jobs
Developing Code for Data Processing using Python and SQL	- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark 1. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 2. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 3. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI 4. Explain the advantages and disadvantages of streaming tables compared to materialized views 5. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 6. Create pipeline components using control flow operators such as if/else and foreach 7. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 8. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines - Using Python and Tools for Development 1. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration 2. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives 3. Develop User-Defined Functions using Pandas/Python UDF
Monitoring and Alerting	- Monitoring 1. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines 2. Use Lakeflow Declarative Pipelines event logs to monitor pipelines 3. Use Query Profile and Spark UI to monitor workloads 4. Use system tables for observability of resource utilization, cost, auditing, and workloads - Alerting 1. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues 2. Use SQL Alerts to monitor data quality
Data Sharing and Federation	- Share and federate data 1. Use Delta Sharing to share live data from the Lakehouse with any computing platform 2. Configure Lakehouse Federation with appropriate governance across supported source systems 3. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol

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無料PDFCertified-Data-Engineer-Professional関連資格試験対応 & 最高のDatabricks 認定トレーニング-更新Databricks Databricks Certified Data Engineer Professional

Databricks高品質で、高い合格率とヒット率を高めることができるCertified-Data-Engineer-ProfessionalのDatabricks Certified Data Engineer Professional試験トレントを提供します。当社の合格率は99％であるため、当社の製品を購入し、Certified-Data-Engineer-Professional試験の教材によってもたらされるメリットを享受することができます。当社の製品は効率的で、短時間でDatabricks Certified Data Engineer Professionalガイド急流を習得し、エネルギーを節約するのに役立ちます。当社が提供する製品は専門家によって編集され、It-Passports深い経験を後押しする専門家によって承認されています。シラバスの変更および理論と実践の最新の開発状況に応じて改訂および更新されます。

Databricks Certified Data Engineer Professional 認定 Certified-Data-Engineer-Professional 試験問題 (Q57-Q62):

質問 # 57
A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

A. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
B. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
C. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
D. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.

正解：C

解説：
Maintaining data quality rules in a centralized Delta table allows for the reuse of these rules across multiple DLT (Delta Live Tables) pipelines. By storing these rules outside the pipeline's target schema and referencing the schema name as a pipeline parameter, the team can apply the same set of data quality checks to different tables within the pipeline. This approach ensures consistency in data quality validations and reduces redundancy in code by not having to replicate the same rules in each DLT notebook or file.

質問 # 58
A data engineer is running a groupBy aggregation on a massive user activity log grouped by user_id. A few users have millions of records, causing task skew and long runtimes. Which technique will fix the skew in this aggregation?

A. Filter out the skewed users before the aggregation.
B. Increase the Spark driver memory and retry.
C. Use reduceByKey instead of groupBy to avoid shuffles.
D. Use salting by adding a random prefix to skewed keys before aggregation, then aggregate again after removing the prefix.

正解：D

解説：
Salting distributes records for heavily skewed keys across multiple partitions by adding a random prefix, which balances task execution during the aggregation. A second aggregation after removing the prefix correctly recombines the partial results, eliminating skew-related bottlenecks without losing accuracy.

質問 # 59
A data engineer is building a customer data pipeline in Lakeflow Spark Declarative Pipelines. The source is a cloud-based event stream with limited retention containing inserts, updates, and deletes for customer records. These changes are being applied using the AUTO CDC INTO syntax to maintain an SCD Type 1 table as the target table, customer_dim. How should the data engineer build a downstream job that streams from the customer_dim table to only act on updates and delete events, processing data incrementally?

A. Use ignoreChanges flag while streaming from customer_dim to avoid breaking the pipeline during updates and deletes.
B. Streaming from customer_dim table would only be possible in the case of SCD 2 retention.
C. When stored as SCD 1, the target of AUTO CDC INTO includes updates and deletes. Streaming from customer_dim can fail due to these operations. Instead, build another stream from the original source.
D. Read change data feed from customer_dim table and apply filters to incrementally act on the change events.

正解：D

解説：
Reading the change data feed from the customer_dim table enables downstream processing to react specifically to update and delete events while operating incrementally. Change data feed exposes row-level change types and versions, making it the correct mechanism for streaming only the relevant changes from an SCD Type 1 table maintained with AUTO CDC INTO.

質問 # 60
A workspace admin has created a new catalog called finance_data and wants to delegate permission management to a finance team lead without giving them full admin rights. Which privilege should be granted to the finance team lead?

A. Make the finance team lead a metastore admin.
B. ALL PRIVILEGES on the finance_data catalog.
C. GRANT OPTION privilege on the finance_data catalog.
D. MANAGE privilege on the finance_data catalog.

正解：D

解説：
The MANAGE privilege in Unity Catalog provides the ability to grant and revoke privileges on the specified object (in this case, a catalog) without giving full administrative access or ownership.
This is the Databricks-recommended approach for delegating governance responsibilities while preserving the principle of least privilege.
By contrast, the ALL PRIVILEGES option grants excessive access (including read and write permissions), and metastore admin status provides global control over all catalogs--far exceeding the requirement. The MANAGE privilege enables the finance team lead to control access to objects within finance_data responsibly while limiting overall administrative exposure.

質問 # 61
Which statement describes a key benefit of an end-to-end test?

A. It makes it easier to automate your test suite
B. It closely simulates real world usage of your application.
C. It pinpoint errors in the building blocks of your application.
D. It provides testing coverage for all code paths and branches.

正解：B

解説：
End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real- world, user behavior, ensuring that the system as a whole operates correctly.

質問 # 62
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