Databricks-Certified-Professional-Data-Engineer題庫資料，Databricks-Certified-Professional-Data-Engineer測試引擎

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

Section	Weight	Objectives
Topic 1: Debugging and Deploying	10%	- CI/CD and DevOps practices - Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs
Topic 2: Data Ingestion & Acquisition	7%	- Auto Loader and streaming ingestion - Connecting to diverse data sources - Schema inference and evolution
Topic 3: Data Sharing and Federation	5%	- Cross-workspace and cross-cloud access - Unity Catalog data sharing
Topic 4: 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
Topic 5: Cost & Performance Optimisation	13%	- Query optimization and caching - Cluster configuration and scaling - Storage optimization (partitioning, Z-order, indexing)
Topic 6: Monitoring and Alerting	10%	- Setting up alerts and notifications - Performance and health monitoring - Pipeline observability and logging
Topic 7: Data Transformation, Cleansing, and Quality	10%	- Data validation and quality checks - Handling missing or inconsistent data - Standardization and normalization
Topic 8: Data Modelling	6%	- Medallion Architecture implementation - Delta Lake table design - Schema design and management
Topic 9: Ensuring Data Security and Compliance	10%	- Data encryption and masking - Compliance standards implementation - Access control and permissions
Topic 10: Data Governance	7%	- Data lineage and metadata tracking - Unity Catalog management - Policy enforcement

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Databricks Databricks-Certified-Professional-Data-Engineer測試引擎 & Databricks-Certified-Professional-Data-Engineer題庫下載

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最新的 Databricks Certification Databricks-Certified-Professional-Data-Engineer 免費考試真題 (Q15-Q20):

問題 #15
A query is taking too long to run. After investigating the Spark UI, the data engineer discovered a significant amount of disk spill . The compute instance being used has a core-to-memory ratio of 1:2.
What are the two steps the data engineer should take to minimize spillage? (Choose 2 answers)

A. Choose a compute instance with more network bandwidth.
B. Choose a compute instance with a higher core-to-memory ratio.
C. Reduce spark.sql.files.maxPartitionBytes.
D. Choose a compute instance with more disk space.
E. Increase spark.sql.files.maxPartitionBytes.

答案：B,C

解題說明：
Databricks recommends addressing disk spilling -which occurs when Spark tasks run out of memory-by increasing memory per core and controlling partition size. Selecting an instance type with a higher memory- to-core ratio (A) provides each task with more available RAM, directly reducing the chance of spilling to disk. Additionally, reducing spark.sql.files.maxPartitionBytes (D) creates smaller partitions, preventing any single task from holding too much data in memory. Increasing partition size (C) or disk capacity (B) does not solve memory bottlenecks, and bandwidth (E) affects network I/O, not spill behavior. Therefore, the correct actions are A and D .

問題 #16
You are asked to write a python function that can read data from a delta table and return the Data-Frame, which of the following is correct?

A. Write SQL UDF that can return tabular data
B. Python function cannot return a DataFrame
C. Python function can return a DataFrame
D. Write SQL UDF to return a DataFrame
E. Python function will result in out of memory error due to data volume

答案：E

解題說明：
Explanation
The answer is Python function can return a DataFrame
The function would something like this,
1.get_source_dataframe(tablename):
2. df = spark.read.table(tablename)
3.return df
df = get_source_dataframe('test_table')
since there is no action spark returns a Dataframe and assigns to df python variable

問題 #17
A data architect is designing a data model that works for both video-based machine learning work-loads and
highly audited batch ETL/ELT workloads.
Which of the following describes how using a data lakehouse can help the data architect meet the needs of
both workloads?

A. A data lakehouse combines compute and storage for simple governance
B. A data lakehouse stores unstructured data and is ACID-compliant
C. A data lakehouse provides autoscaling for compute clusters
D. A data lakehouse requires very little data modeling
E. A data lakehouse fully exists in the cloud

答案：B

問題 #18
An engineering manager uses a Databricks SQL query to monitor their team's progress on fixes related to
customer-reported bugs. The manager checks the results of the query every day, but they are manually
rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are up-dated each
day?

A. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL
B. They can schedule the query to run every 1 day from the Jobs UI
C. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL
D. They can schedule the query to run every 12 hours from the Jobs UI
E. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL

答案：E

問題 #19
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline.
Which command should the data engineer enter in a web terminal configured with the Databricks CLI?

A. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command
B. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
C. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline
D. Stop the existing pipeline; use the returned settings in a reset command

答案：A

解題說明：
The Databricks CLI provides a way to automate interactions with Databricks services. When dealing with pipelines, you can use the databricks pipelines get --pipeline-id command to capture the settings of an existing pipeline in JSON format. This JSON can then be modified by removing the pipeline_id to prevent conflicts and renaming the pipeline to create a new pipeline. The modified JSON file can then be used with the databricks pipelines create command to create a new pipeline with those settings.
Reference:
Databricks Documentation on CLI for Pipelines: Databricks CLI - Pipelines

問題 #20
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