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

Section	Weight	Objectives
Topic 1: Data Processing	28%	- Data Transformation - Spark SQL - ETL Pipelines - Structured Streaming
Topic 2: Databricks Lakehouse Platform	24%	- Lakehouse Architecture - Delta Lake - Unity Catalog - Data Management
Topic 3: Data Quality and Governance	12%	- Data Quality - Governance - Data Lineage
Topic 4: Data Modeling and Storage	20%	- Data Modeling - File Formats - Storage Optimization
Topic 5: Monitoring and Troubleshooting	16%	- Troubleshooting - Performance Optimization - Monitoring

>> Databricks-Certified-Data-Engineer-Professional最新な問題集 <<

Databricks Databricks-Certified-Data-Engineer-Professional模試エンジン & Databricks-Certified-Data-Engineer-Professional資格認定

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q176-Q181):

質問 # 176
A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

A. Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.
B. Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
C. Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
D. The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
E. Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.

正解：B

解説：
This is the correct answer because it describes how Delta Lake can help to avoid data loss of this nature in the future. By ingesting all raw data and metadata from Kafka to a bronze Delta table, Delta Lake creates a permanent, replayable history of the data state that can be used for recovery or reprocessing in case of errors or omissions in downstream applications or pipelines.
Delta Lake also supports schema evolution, which allows adding new columns to existing tables without affecting existing queries or pipelines. Therefore, if a critical field was omitted from an application that writes its Kafka source to Delta Lake, it can be easily added later and the data can be reprocessed from the bronze table without losing any information.

質問 # 177
The data governance team has instituted a requirement that the "user" table containing Personal Identifiable Information (PII) must have the appropriate masking on the SSN column. This means that anyone outside of the HRAdminGroup should see masked social security numbers as ***-**-
****.
The team created a masking function:

What does the data governance team need to do next to achieve this goal?

A. CREATE TABLE users
(name STRING);
ALTER TABLE users CREATE COLUMN ssn CREATE MASK ssn_mask;
B. CREATE TABLE users
(name STRING, ssn INT MASKED ssn_mask);
C. CREATE TABLE users
(name STRING, ssn STRING);
ALTER TABLE users ALTER COLUMN ssn SET MASK ssn_mask;
D. CREATE TABLE users
(name STRING, int STRING);
ALTER TABLE users ALTER COLUMN ssn CREATE MASK if is_member('HRAdminGroup');

正解：C

解説：
In Databricks, after creating a masking function, you apply it to a column using ALTER TABLE
<table> ALTER COLUMN <column> SET MASK <mask_function>. The table must already include the column (here, ssn as STRING). This ensures that only users in the HRAdminGroup see the unmasked SSN, while all others see the masked value.

質問 # 178
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from part- file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

A. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
B. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), and then write to parquet.
C. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
D. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
E. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.

正解：D

解説：
The key to efficiently converting a large JSON dataset to Parquet files of a specific size without shuffling data lies in controlling the size of the output files directly. Setting spark.sql.files.maxPartitionBytes to 512 MB configures Spark to process data in chunks of 512 MB. This setting directly influences the size of the part-files in the output, aligning with the target file size.
Narrow transformations (which do not involve shuffling data across partitions) can then be applied to this data.
Writing the data out to Parquet will result in files that are approximately the size specified by spark.sql.files.maxPartitionBytes, in this case, 512 MB. The other options involve unnecessary shuffles or repartitions (B, C, D) or an incorrect setting for this specific requirement (E).

質問 # 179
A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses. Which design approach for the date dimension will support efficient time-based querying and aggregation?

A. Store only the date value and calculate all time attributes in queries.
B. Store the date as string in ISO format (YYYY-MM-DD) for readability.
C. Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.
D. Create separate dimension tables for different calendar systems (fiscal, academic, etc.)

正解：C

解説：
Pre-calculating commonly used time attributes in the date dimension enables efficient filtering, grouping, and aggregation without requiring repeated computations at query time. This design supports a wide range of time-based analyses and improves query performance and usability in dimensional models.

質問 # 180
A Data Engineer is building a simple data pipeline using Lakeflow Declarative Pipelines (LDP) in Databricks to ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create Lakeflow Declarative Pipelines that read the raw JSON data and write it into a Delta table for further processing. Which code snippet will correctly ingest the raw JSON data and create a Delta table using LDP?

A. import dlt
@dlt.table
def raw_customers():
return spark.read.format("csv").load("s3://my-bucket/raw-customers/")
B. import dlt
@dlt.view
def raw_customers():
return spark.format.json("s3://my-bucket/raw-customers/")
C. import dlt
@dlt.table
def raw_customers():
return spark.read.format("parquet").load("s3://my-bucket/raw-customers/")
D. import dlt
@dlt.table
def raw_customers():
return spark.read.json("s3://my-bucket/raw-customers/")

正解：D

解説：
The correct method to define a table using Lakeflow Declarative Pipelines (LDP) is with the
@dlt.table decorator, which persists the output as a managed Delta table. When ingesting raw JSON data, spark.read.json() or spark.read.format("json").load() is the standard approach. This reads JSON- formatted files from the source and stores them in Delta format automatically managed by Databricks.

質問 # 181
......

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