Databricks-Certified-Professional-Data-Engineer合格内容、Databricks-Certified-Professional-Data-Engineer日本語版対応参考書

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

Section	Weight	Objectives
Data Governance	7%	- Unity Catalog management - Data lineage and metadata tracking - Policy enforcement
Data Sharing and Federation	5%	- Cross-workspace and cross-cloud access - Unity Catalog data sharing
Monitoring and Alerting	10%	- Setting up alerts and notifications - Performance and health monitoring - Pipeline observability and logging
Data Ingestion & Acquisition	7%	- Connecting to diverse data sources - Auto Loader and streaming ingestion - Schema inference and evolution
Data Modelling	6%	- Delta Lake table design - Schema design and management - Medallion Architecture implementation
Developing Code for Data Processing using Python and SQL	22%	- Data transformation and aggregation - Integration with Databricks APIs and tools - Batch and incremental processing logic
Ensuring Data Security and Compliance	10%	- Access control and permissions - Compliance standards implementation - Data encryption and masking
Data Transformation, Cleansing, and Quality	10%	- Standardization and normalization - Handling missing or inconsistent data - Data validation and quality checks
Cost & Performance Optimisation	13%	- Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching
Debugging and Deploying	10%	- Troubleshooting pipelines and errors - CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs

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Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q115-Q120):

質問 # 115
Which of the following statements can successfully read the notebook widget and pass the python variable to a SQL statement in a Python notebook cell?

A. 1.order_date = dbutils.widgets.get("widget_order_date")
2.
3.spark.sql("SELECT * FROM sales WHERE orderDate = order_date")
B. 1.order_date = dbutils.widgets.get("widget_order_date")
2.
3.spark.sql(f"SELECT * FROM sales WHERE orderDate = '${order_date }' ")
C. 1.order_date = dbutils.widgets.get("widget_order_date")
2.
3.spark.sql(f"SELECT * FROM sales WHERE orderDate = '{order_date}' ")
(Correct)
D. 1.order_date = dbutils.widgets.get("widget_order_date")
2.
3.spark.sql(f"SELECT * FROM sales WHERE orderDate = 'order_date' ")
E. 1.order_date = dbutils.widgets.get("widget_order_date")
2.
3.spark.sql(f"SELECT * FROM sales WHERE orderDate = 'f{order_date }'")

正解：C

質問 # 116
The team has decided to take advantage of table properties to identify a business owner for each table, which of the following table DDL syntax allows you to populate a table property identifying the business owner of a table CREATE TABLE inventory (id INT, units FLOAT)

A. CREATE TABLE inventory (id INT, units FLOAT)
SET PROPERTY (business_owner = 'supply chain')
B. CREATE TABLE inventory (id INT, units FLOAT)
SET (business_owner = 'supply chain')
C. SET TBLPROPERTIES business_owner = 'supply chain'
CREATE TABLE inventory (id INT, units FLOAT)
D. TBLPROPERTIES (business_owner = 'supply chain')
E. CREATE TABLE inventory (id INT, units FLOAT)
SET TAG (business_owner = 'supply chain')

正解：D

解説：
Explanation
CREATE TABLE inventory (id INT, units FLOAT) TBLPROPERTIES (business_owner = 'supply chain') Table properties and table options (Databricks SQL) | Databricks on AWS Alter table command can used to update the TBLPROPERTIES ALTER TABLE inventory SET TBLPROPERTIES(business_owner , 'operations')

質問 # 117
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source table has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A. An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.
B. An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.
C. A batch job will update the gold_customer_lifetime_sales_summary table, replacing only those rows that have different values than the current version of the table, using customer_id as the primary key.
D. The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.
E. The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.

正解：E

解説：
This code is using the pyspark.sql.functions library to group the silver_customer_sales table by customer_id and then aggregate the data using the minimum sale date, maximum sale total, and sum of distinct order ids. The resulting aggregated data is then written to the gold_customer_lifetime_sales_summary table, overwriting any existing data in that table. This is a batch job that does not use any incremental or streaming logic, and does not perform any merge or update operations. Therefore, the code will overwrite the gold table with the aggregated values from the silver table every time it is executed. Reference:
https://docs.databricks.com/spark/latest/dataframes-datasets/introduction-to-dataframes-python.html
https://docs.databricks.com/spark/latest/dataframes-datasets/transforming-data-with-dataframes.html
https://docs.databricks.com/spark/latest/dataframes-datasets/aggregating-data-with-dataframes.html

質問 # 118
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings.
The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
B. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
C. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.
D. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.

正解：D

解説：
Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. Thesestatistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.References: Databricks documentation on Delta Lake optimization techniques, including data skipping and statistics collection (https://docs.databricks.com/delta/optimizations
/index.html).

質問 # 119
A data engineer is implementing Unity Catalog governance for a multi-team environment. Data scientists need interactive clusters for basic data exploration tasks, while automated ETL jobs require dedicated processing.
How should the data engineer configure cluster isolation policies to enforce least privilege and ensure Unity Catalog compliance?

A. Create compute policies with STANDARD access mode for interactive workloads and DEDICATED access mode for automated jobs.
B. Configure all clusters with NO ISOLATION_SHARED access mode since Unity Catalog works with any cluster configuration.
C. Use only DEDICATED access mode for both interactive workloads and automated jobs to maximize security isolation.
D. Allow all users to create any cluster type and rely on manual configuration to enable Unity Catalog access modes.

正解：A

解説：
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Unity Catalog enforces governance and data isolation through cluster access modes and compute policies. According to Databricks documentation, "Interactive clusters that multiple users share should use Standard access mode, while automated jobs and production pipelines should use Dedicated access mode for stricter isolation." Standard access mode allows multiple users to share the same compute resources but still respects Unity Catalog permissions. Dedicated access mode isolates the job run's execution environment, ensuring that data access is limited to the job's identity. Configuring these modes within compute policies enforces least privilege and ensures all compute complies with Unity Catalog security standards. Options A and C are incorrect because using only Dedicated clusters reduces resource efficiency, while "No isolation" clusters are not Unity Catalog compliant.

質問 # 120
......

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