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

Section	Objectives
Data Ingestion and Processing	- Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader - ETL pipeline design patterns
Production Pipelines and Orchestration	- Error handling and recovery strategies - Job scheduling and monitoring - Databricks Workflows
Delta Lake and Data Management	- Delta Lake transactions and ACID properties - Time travel and versioning - Schema evolution and enforcement
Databricks Lakehouse Platform Architecture	- Workspace and cluster architecture - Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics)
Data Modeling and Transformation	- Dimensional modeling concepts - Performance optimization techniques - Spark SQL transformations

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Databricks-Certified-Data-Engineer-Professional試験解答 & Databricks-Certified-Data-Engineer-Professionalテストサンプル問題

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q18-Q23):

質問 # 18
Which statement regarding spark configuration on the Databricks platform is true?

A. Spark configuration properties can only be set for an interactive cluster by creating a global init script.
B. Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.
C. Spark configuration set within an notebook will affect all SparkSession attached to the same interactive cluster
D. When the same spar configuration property is set for an interactive to the same interactive cluster.
E. The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs.

正解：B

解説：
When Spark configuration properties are set for an interactive cluster using the Clusters UI in Databricks, those configurations are applied at the cluster level. This means that all notebooks attached to that cluster will inherit and be affected by these configurations. This approach ensures consistency across all executions within that cluster, as the Spark configuration properties dictate aspects such as memory allocation, number of executors, and other vital execution parameters. This centralized configuration management helps maintain standardized execution environments across different notebooks, aiding in debugging and performance optimization.

質問 # 19
A member of the data engineering team has submitted a short notebook that they wish to schedule as part of a larger data pipeline. Assume that the commands provided below produce the logically correct results when run as presented.

Which command should be removed from the notebook before scheduling it as a job?

A. Cmd 5
B. Cmd 2
C. Cmd 6
D. Cmd 3
E. Cmd 4

正解：C

解説：
When scheduling a Databricks notebook as a job, it's generally recommended to remove or modify commands that involve displaying output, such as using the display() function. Displaying data using display() is an interactive feature designed for exploration and visualization within the notebook interface and may not work well in a production job context.
The finalDF.explain() command, which provides the execution plan of the DataFrame transformations and actions, is often useful for debugging and optimizing queries. While it doesn't display interactive visualizations like display(), it can still be informative for understanding how Spark is executing the operations on your DataFrame.

質問 # 20
The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evolution enabled on this table.

How would the following update be handled?

A. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
B. The update throws an error because changes to existing columns in the target schema are not supported.
C. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
D. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.

正解：D

解説：
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.

質問 # 21
A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?

A. Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
B. Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
C. Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.
D. Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.
E. Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.

正解：A

解説：
https://www.databricks.com/blog/2017/05/22/running-streaming-jobs-day-10x-cost-savings.html

質問 # 22
A table is registered with the following code:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
B. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
C. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
D. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
E. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.

正解：B

解説：
Table is created and data of join will be stored on DBFS and it will be returned on query time.

質問 # 23
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