Certified-Data-Engineer-Professional無料過去問 & Certified-Data-Engineer-Professional日本語版

多種多様なユーザーのニーズを促進するために、Certified-Data-Engineer-Professionalスタディガイドでは、現在最高の適用率を持つ3つのモデル（PDF、ソフトウェア、オンライン）を開発しました。別の名前のオンラインモードは、学習教材のアプリです。ブラウザ上のユーザー端末が、この学習モデルの教材をシミュレートするCertified-Data-Engineer-Professionalによって適用されたアプリケーションを実現できる限り、Webブラウザーに基づいて開発されます、ユーザーはアプリのリンクを開くだけで済み、Certified-Data-Engineer-Professional学習教材の方法で学習コンテンツをリアルタイムですばやく開くことができます。

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

SectionObjectives
Topic 1: Data Modeling- Design and optimize data models
  • 1. Simplify data layout decisions and optimize query performance using liquid clustering
    • 2. Identify the benefits of liquid clustering over partitioning and Z-Ordering
      • 3. Design dimensional models for analytical workloads with efficient querying and aggregation
        • 4. Design and implement scalable data models using Delta Lake to manage large datasets
          Topic 2: Monitoring and Alerting- Alerting
          • 1. Use SQL Alerts to monitor data quality
            • 2. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues
              - 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 system tables for observability of resource utilization, cost, auditing, and workloads
                    • 4. Use Query Profile and Spark UI to monitor workloads
                      Topic 3: Cost & Performance Optimization- Optimize cost and performance
                      • 1. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning
                        • 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. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling
                                Topic 4: 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
                                    Topic 5: 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 ACLs to secure workspace objects and enforce the principle of least privilege
                                          • 2. Use row filters and column masks to protect sensitive table data
                                            • 3. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization
                                              Topic 6: Data Transformation, Cleansing, and Quality- Transform and validate data
                                              • 1. Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or Auto Loader in classic jobs
                                                • 2. Write efficient Spark SQL and PySpark code for advanced transformations including window functions, joins, and aggregations
                                                  Topic 7: Data Sharing and Federation- Share and federate data
                                                  • 1. Configure Lakehouse Federation with appropriate governance across supported source systems
                                                    • 2. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol
                                                      • 3. Use Delta Sharing to share live data from the Lakehouse with any computing platform
                                                        Topic 8: Debugging and Deploying- Deploying CI/CD
                                                        • 1. Build and deploy Databricks resources using Databricks Asset Bundles
                                                          • 2. Configure and integrate Git-based CI/CD workflows using Databricks Git folders for notebook and code deployment
                                                            - Debugging and Troubleshooting
                                                            • 1. Analyze errors and remediate failed job runs using job repairs and parameter overrides
                                                              • 2. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines
                                                                • 3. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors
                                                                  Topic 9: Data Ingestion & Acquisition- Design and implement data ingestion pipelines
                                                                  • 1. Ingest formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, text, and binary data from sources such as message buses and cloud storage
                                                                    • 2. Create an append-only data pipeline capable of handling both batch and streaming data using Delta
                                                                      Topic 10: Developing Code for Data Processing using Python and SQL- Using Python and Tools for Development
                                                                      • 1. Develop User-Defined Functions using Pandas/Python UDF
                                                                        • 2. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration
                                                                          • 3. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives
                                                                            - Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark
                                                                            • 1. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines
                                                                              • 2. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools
                                                                                • 3. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior
                                                                                  • 4. Create pipeline components using control flow operators such as if/else and foreach
                                                                                    • 5. Explain the advantages and disadvantages of streaming tables compared to materialized views
                                                                                      • 6. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI
                                                                                        • 7. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines
                                                                                          • 8. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader

                                                                                            >> Certified-Data-Engineer-Professional無料過去問 <<

                                                                                            Certified-Data-Engineer-Professional日本語版、Certified-Data-Engineer-Professional的中率

                                                                                            IT業界での大手会社として、Databricksは認証を通して専門家の標準を確認しました。認証を取得した専門家たちの給料は普通の専門家たちに比べて高いです。だから、Certified-Data-Engineer-Professional試験の認証はIT業界でのあなたにとって重要です。この認証がありましたら、あなたはもっと輝かしい未来を迎えることができます。Certified-Data-Engineer-Professional問題集の重要性が言うまでもなく、Certified-Data-Engineer-Professional問題集の選択も大切です。我々の問題集を利用して、試験に合格することができます。

                                                                                            Databricks Certified Data Engineer Professional 認定 Certified-Data-Engineer-Professional 試験問題 (Q119-Q124):

                                                                                            質問 # 119
                                                                                            A data engineering team needs to create a SQL Alert that monitors data quality across multiple columns in their customer table. They want to trigger an alert when both the percentage of customers with missing email addresses exceeds 15% AND the percentage of customers with invalid phone number formats exceeds 10%. Which SQL query pattern is appropriate for implementing this multi-column alert condition?

                                                                                            正解：D

                                                                                            解説：
                                                                                            This pattern computes independent percentage metrics for each data quality condition in a single aggregated query. By calculating the percentage of missing emails and invalid phone formats as separate columns, it enables the SQL Alert to evaluate a compound condition where both thresholds must be exceeded before triggering.


                                                                                            質問 # 120
                                                                                            Which statement characterizes the general programming model used by Spark Structured Streaming?

                                                                                            正解：E

                                                                                            解説：
                                                                                            The key idea in Structured Streaming is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model. You will express your streaming computation as standard batch-like query as on a static table, and Spark runs it as an incremental query on the unbounded input table. Let's understand this model in more detail.
                                                                                            https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html


                                                                                            質問 # 121
                                                                                            A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete. Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?

                                                                                            正解：B

                                                                                            解説：
                                                                                            The Catalog Explorer provides an AI-powered "AI Generate" capability that automatically creates intelligent column descriptions by analyzing column names, data types, sample values, and observed data patterns. This approach enables rapid, scalable documentation of existing tables, significantly improving data discoverability and compliance without manual effort.


                                                                                            質問 # 122
                                                                                            A Delta Lake table was created with the below query:

                                                                                            Consider the following query:
                                                                                            DROP TABLE prod.sales_by_store
                                                                                            If this statement is executed by a workspace admin, which result will occur?

                                                                                            正解：E

                                                                                            解説：
                                                                                            When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage.


                                                                                            質問 # 123
                                                                                            Which Python variable contains a list of directories to be searched when trying to locate required modules?

                                                                                            正解：B

                                                                                            解説：
                                                                                            sys. path is a built-in variable within the sys module. It contains a list of directories that the interpreter will search in for the required module.


                                                                                            質問 # 124
                                                                                            ......

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