Sample Databricks-Certified-Professional-Data-Engineer Exam, Databricks-Certified-Professional-Data-Engineer Valid Braindumps Questions

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

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

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Databricks Certified Professional Data Engineer Exam practice dumps & Databricks-Certified-Professional-Data-Engineer exam dumps

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q175-Q180):

NEW QUESTION # 175
A data engineering team is migrating off its legacy Hadoop platform. As part of the process, they are evaluating storage formats for performance comparison. The legacy platform uses ORC and RCFile formats.
After converting a subset of data to Delta Lake , they noticed significantly better query performance. Upon investigation, they discovered that queries reading from Delta tables leveraged a Shuffle Hash Join , whereas queries on legacy formats used Sort Merge Joins . The queries reading Delta Lake data also scanned less data.
Which reason could be attributed to the difference in query performance?

A. The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.
B. The queries against the Delta Lake tables were able to leverage the dynamic file pruning optimization.
C. Shuffle Hash Joins are always more efficient than Sort Merge Joins.
D. Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.

Answer: D

Explanation:
Delta Lake outperforms legacy Hadoop formats because it leverages Parquet-based storage , data skipping
, and file pruning . According to Databricks documentation, Delta Lake automatically stores detailed statistics (min/max values and file-level metadata) in the transaction log. During query planning, the engine uses these statistics to skip entire files that do not match query filters , a process called data skipping and file pruning . Additionally, Delta uses a vectorized Parquet reader , which reduces I/O and CPU overhead.
Together, these optimizations allow Delta to scan significantly less data and produce more efficient physical query plans (e.g., Shuffle Hash Join instead of Sort Merge Join). The performance gain is due to efficient data skipping, not the inherent superiority of join type.

NEW QUESTION # 176
A data engineering manager has noticed that each of the queries in a Databricks SQL dashboard takes a few
minutes to update when they manually click the "Refresh" button. They are curious why this might be
occurring, so a team member provides a variety of reasons on why the delay might be occurring.
Which of the following reasons fails to explain why the dashboard might be taking a few minutes to update?

A. The queries attached to the dashboard might take a few minutes to run under normal circumstances
B. The queries attached to the dashboard might all be connected to their own, unstarted Databricks clusters
C. The Job associated with updating the dashboard might be using a non-pooled endpoint
D. The SQL endpoint being used by each of the queries might need a few minutes to start up
E. The queries attached to the dashboard might first be checking to determine if new data is available

Answer: C

NEW QUESTION # 177
Direct query on external files limited options, create external tables for CSV files with header and pipe delimited CSV files, fill in the blanks to complete the create table statement CREATE TABLE sales (id int, unitsSold int, price FLOAT, items STRING)
________
________
LOCATION "dbfs:/mnt/sales/*.csv"

A. USING CSV
OPTIONS ( header ="true", delimiter = "|")
(Correct)
B. FORMAT CSV
TYPE ( header ="true", delimiter = "|")
C. USING CSV
TYPE ( "true","|")
D. FORMAT CSV
OPTIONS ( "true","|")
E. FORMAT CSV
FORMAT TYPE ( header ="true", delimiter = "|")

Answer: A

Explanation:
Explanation
Answer is
USING CSV
OPTIONS ( header ="true", delimiter = "|")
Here is the syntax to create an external table with additional options
CREATE TABLE table_name (col_name1 col_typ1,..)
USING data_source
OPTIONS (key='value', key2=vla2)
LOCATION = "/location"

NEW QUESTION # 178
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 dynamically in queries.
B. Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.
C. Create separate dimension tables for different calendar systems (fiscal, academic, etc.).
D. Store the date as a string in the format YYYY-MM-DD for readability.

Answer: B

Explanation:
In dimensional modeling, Databricks recommends denormalized, attribute-rich dimension tables for performance and usability. A date dimension should include all commonly used derived time attributes such as fiscal period, quarter, month, weekday, and holiday flags. Precomputing these attributes ensures consistent business logic, eliminates repeated calculations during query time, and enables efficient filtering and aggregation. The documentation for Delta Lake and Lakehouse design explicitly advises precomputing these attributes for analytical workloads that depend heavily on time-based slicing. Options A and C degrade performance and consistency, while maintaining multiple calendar-specific dimension tables (B) complicates the model unnecessarily.

NEW QUESTION # 179
A data engineer is designing a pipeline in Databricks that processes records from a Kafka stream where late- arriving data is common.
Which approach should the data engineer use?

A. Use a watermark to specify the allowed lateness to accommodate records that arrive after their expected window, ensuring correct aggregation and state management.
B. Implement a custom solution using Databricks Jobs to periodically reprocess all historical data.
C. Use an Auto CDC pipeline with batch tables to simplify late data handling.
D. Use batch processing and overwrite the entire output table each time to ensure late data is incorporated correctly.

Answer: A

Explanation:
In Structured Streaming, event-time watermarks control how long the engine waits for late-arriving data before finalizing aggregations. By setting an appropriate watermark, Databricks can handle late data gracefully - incorporating records that arrive within the defined window while discarding excessively delayed events.
This approach ensures accurate aggregations, minimizes state size, and prevents memory leaks.
Manual reprocessing (A) or overwriting entire datasets (B) is inefficient and costly, while Auto CDC (C) is used for change tracking in Delta tables, not for streaming event lateness.
Thus, using watermarking is the recommended and official approach for managing late data in streaming pipelines.

NEW QUESTION # 180
......

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