Databricks-Certified-Professional-Data-Engineer최신시험대비자료, Databricks-Certified-Professional-Data-Engineer시험패스보장덤프

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

Section	Objectives
Topic 1: Data Ingestion and Transformation	- Ingest data using Apache Spark and Databricks - Handle batch and streaming data pipelines - Transform and clean datasets using Spark SQL and DataFrame APIs
Topic 2: Security, Governance, Monitoring, and Optimization	- Cost optimization and performance tuning - Monitor and optimize Spark workloads - Implement Unity Catalog governance and access control
Topic 3: Data Modeling and Storage	- Delta Lake table design and optimization - Schema evolution and data partitioning strategies - Design scalable data lakehouse architectures
Topic 4: Production Pipelines and Orchestration	- Pipeline reliability and fault tolerance - Automate ETL pipelines and scheduling - Build and manage workflows using Databricks Jobs

>> Databricks-Certified-Professional-Data-Engineer최신 시험대비자료 <<

최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q225-Q230):

질문 # 225
How to determine if a table is a managed table vs external table?

A. All external tables are stored in data lake, managed tables are stored in DELTA lake
B. Run IS_MANAGED('table_name') function
C. Run SQL command SHOW TABLES to see the type of the table
D. All managed tables are stored in unity catalog
E. Run SQL command DESCRIBE EXTENDED table_name and check type

정답：E

설명：
Explanation
The answer is Run SQL command DESCRIBE EXTENDED table_name and check type Example of External table Graphical user interface, text, application Description automatically generated

Example of managed table
Graphical user interface, text, application, Teams Description automatically generated

질문 # 226
A data engineer is reviewing a failed Lakeflow Jobs run that contains five tasks arranged in a linear DAG:
Task A → Task B → Task C → Task D → Task E.
The run history panel shows Task A completed successfully, Task B is marked Failed with an exception type of:
AnalysisException: Column 'event_ts' does not exist
Tasks C, D, and E are each marked Upstream failed.
The cluster that executed Task B terminated normally after the task ended.
What should the data engineer do to identify the root cause of the failure before attempting a repair run?

A. Open the run history panel for Tasks C, D, and E and examine their individual error messages to determine which downstream task introduced the column reference that caused the failure.
B. Trigger a full job repair run immediately so that Task B re-executes against the latest data, then monitor whether the AnalysisException recurs before investigating the schema.
C. Inspect the cluster event log for Task B's cluster to find the node eviction or preemption event that caused the task to stop, then check whether the cluster type supports the schema being read.
D. Review the task-level output and stack trace for Task B in the run details panel to confirm the schema mismatch, then trace which upstream data source or transformation removed or renamed the event_ts column.

정답：D

설명：
The run history identifies Task B as the first unsuccessful task in the dependency chain.
Its AnalysisException explicitly indicates that the expected event_ts column cannot be resolved during query analysis.
This typically results from a schema mismatch, renamed field, missing projection, or incorrect transformation logic.
The engineer should inspect Task B's execution output and stack trace to identify the failing statement, then trace the field through its upstream datasets and transformations.
Tasks C, D, and E report upstream failures because their dependency on Task B was not satisfied. Their errors are consequences rather than the original cause.
Option B retries without resolving the underlying problem. Option C investigates downstream symptoms. Option D focuses on infrastructure despite the cluster terminating normally.
Therefore, Option A provides the correct diagnostic approach.
Databricks on AWS

질문 # 227
A junior data engineer on your team has implemented the following code block.

The view new_events contains a batch of records with the same schema as the events Delta table. The event_id field serves as a unique key for this table.
When this query is executed, what will happen with new records that have the same event_id as an existing record?

A. They are merged.
B. They are deleted.
C. They are ignored.
D. They are inserted.
E. They are updated.

정답：C

설명：
This is the correct answer because it describes what will happen with new records that have the same event_id as an existing record when the query is executed. The query uses the INSERT INTO command to append new records from the view new_events to the table events. However, the INSERT INTO command does not check for duplicate values in the primary key column (event_id) and does not perform any update or delete operations on existing records. Therefore, if there are new records that have the same event_id as an existing record, they will be ignored and not inserted into the table events. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Append data using INSERT INTO" section.
"If none of the WHEN MATCHED conditions evaluate to true for a source and target row pair that matches the merge_condition, then the target row is left unchanged." https://docs.databricks.com/en/sql/language-manual/delta-merge-into.html#:~:text=If%20none%20of%20the%20WHEN%20MATCHED%20conditions%20evaluate%20to%20true%20for%20a%20source%20and%20target%20row%20pair%20that%20matches%20the%20merge_condition%2C%20then%20the%20target%20row%20is%20left%20unchanged.

질문 # 228
Which approach demonstrates a modular and testable way to use DataFrame transform for ETL code in PySpark?

A. def upper_transform(df):
return df.withColumn( " value_upper " , upper(col( " value " )))
actual = test_input.transform(upper_transform)
assertDataFrameEqual(actual, expected)
B. def upper_value(df):
return df.withColumn( " value_upper " , upper(col( " value " )))
def filter_positive(df):
return df.filter(df[ " id " ] > 0)
pipeline_df = df.transform(upper_value).transform(filter_positive)
C. def transform_data(input_df):
# transformation logic here
return output_df
test_input = spark.createDataFrame([(1, " a " )], [ " id " , " value " ]) assertDataFrameEqual(transform_data(test_input), expected)
D. class Pipeline:
def transform(self, df):
return df.withColumn( " value_upper " , upper(col( " value " )))
pipeline = Pipeline()
assertDataFrameEqual(pipeline.transform(test_input), expected)

정답：B

설명：
Apache Spark documents DataFrame.transform(func, *args, **kwargs) as concise syntax for chaining custom transformations, where the function takes a DataFrame and returns a DataFrame. The official example explicitly shows chained transforms, which makes option B the most modular and idiomatic ETL design among the choices. ( Apache Spark ) Option D shows a valid single transform and test, but it does not demonstrate the modular pipeline composition aspect as clearly as B. Option A does not actually use DataFrame.transform , and option C wraps logic in a class method but does not demonstrate the documented chaining pattern that transform is designed for. ( Apache Spark )
======

질문 # 229
A data engineer is designing a Lakeflow Declarative Pipeline to process streaming order data. The pipeline uses Auto Loader to ingest data and must enforce data quality by ensuring customer_id and amount are greater than zero. Invalid records should be dropped.
Which Lakeflow Declarative Pipelines configurations implement this requirement using Python?

A. @dlt.table
@dlt.expect("valid_customer", "customer_id IS NOT NULL")
@dlt.expect("valid_amount", "amount > 0")
def silver_orders():
return dlt.read_stream("bronze_orders")
B. @dlt.table
@dlt.expect_or_drop("valid_customer", "customer_id IS NOT NULL")
@dlt.expect_or_drop("valid_amount", "amount > 0")
def silver_orders():
return dlt.read_stream("bronze_orders")
C. @dlt.table
def silver_orders():
return (
dlt.read_stream("bronze_orders")
.expect("valid_customer", "customer_id IS NOT NULL")
.expect("valid_amount", "amount > 0")
)
D. @dlt.table
def silver_orders():
return (
dlt.read_stream("bronze_orders")
.expect_or_drop("valid_customer", "customer_id IS NOT NULL")
.expect_or_drop("valid_amount", "amount > 0")
)

정답：D

설명：
Comprehensive and Detailed Explanation from Databricks Documentation:
Lakeflow Declarative Pipelines (LDP), formerly Delta Live Tables (DLT), supports enforcing data quality using expectations. Expectations can either:
Track violations (expect) → records that do not meet conditions are flagged but still included in the pipeline.
Drop violations (expect_or_drop) → records that do not meet conditions are excluded from downstream tables.
Fail pipeline on violations (expect_or_fail) → records that fail conditions stop the pipeline.
In this scenario, the requirement explicitly states that invalid records (where customer_id is null or amount ≤ 0) must be dropped. According to the official documentation, the correct method is .expect_or_drop("expectation_name", "SQL_predicate") applied on the streaming input.
Option A is correct: It uses .expect_or_drop directly within the transformation chain for both rules, ensuring records that fail are removed before writing to the silver table.
Option B incorrectly uses @dlt.expect decorators, which only track violations but do not drop invalid rows.
Option C uses .expect, which also only flags rows, not drop them.
Option D uses @dlt.expect_or_drop decorator syntax, which is not supported in Python API; expect_or_drop must be applied as a method on the DataFrame, not as a decorator.
Therefore, the correct solution is Option A, which ensures compliance by enforcing data quality and dropping invalid rows programmatically during ingestion.

질문 # 230
......

Databricks Databricks-Certified-Professional-Data-Engineer인증시험에 응시하고 싶으시다면 좋은 학습자료와 학습 가이드가 필요합니다.Databricks Databricks-Certified-Professional-Data-Engineer시험은 it업계에서도 아주 중요한 인증입니다. 시험패스를 원하신다면 충분한 시험준비는 필수입니다.

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Databricks-Certified-Professional-Data-Engineer최신시험대비자료, Databricks-Certified-Professional-Data-Engineer시험패스보장덤프

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

최신 Databricks-Certified-Professional-Data-Engineer최신 시험대비자료 덤프샘플문제 다운로드

최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q225-Q230):