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

Section	Objectives
Topic 1: Databricks Lakehouse Platform Architecture	- Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold) - Workspace and cluster architecture
Topic 2: Data Modeling and Transformation	- Performance optimization techniques - Spark SQL transformations - Dimensional modeling concepts
Topic 3: Delta Lake and Data Management	- Time travel and versioning - Delta Lake transactions and ACID properties - Schema evolution and enforcement
Topic 4: Data Ingestion and Processing	- Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals - ETL pipeline design patterns
Topic 5: Production Pipelines and Orchestration	- Error handling and recovery strategies - Job scheduling and monitoring - Databricks Workflows

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Databricks-Certified-Data-Engineer-Professional덤프문제은행 & Databricks-Certified-Data-Engineer-Professional최신버전 덤프공부문제

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최신 Databricks Certification Databricks-Certified-Data-Engineer-Professional 무료샘플문제 (Q106-Q111):

질문 # 106
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 two.)

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

정답：A,B

설명：
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.

질문 # 107
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Define a view against the products_per_order table and define the dashboard against this view.
B. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
C. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
D. Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.
E. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.

정답：D

질문 # 108
A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each microbatch of data is processed in less than 3s; at least 12 times per minute, a microbatch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution.
Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

A. Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.
B. Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to maximum allowable threshold should minimize this cost.
C. Increase the number of shuffle partitions to maximize parallelism, since the trigger interval cannot be modified without modifying the checkpoint directory.
D. Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.
E. Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.

정답：B

질문 # 109
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bpm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

A. Set the SkipChangeCommits flag to true raw_lot
B. Set the pipelines, reset, allowed property to false on bpm_stats
C. Set the pipelines, reset, allowed property to false on raw_iot
D. Set the skipChangeCommits flag to true on bpm_stats

정답：C

설명：
In Databricks Lakehouse, to retain manually deleted or updated records in the raw_iot table while recomputing downstream tables when a pipeline update is run, the property pipelines.reset.allowed should be set to false. This property prevents the system from resetting the state of the table, which includes the removal of the history of changes, during a pipeline update. By keeping this property as false, any changes to the raw_iot table, including manual deletes or updates, are retained, and recomputation of downstream tables, such as bpm_stats, can occur with the full history of data changes intact.

질문 # 110
The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

A. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
B. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
C. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
D. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.

정답：B

설명：
In Databricks, using the Secrets module allows for secure management of sensitive information such as database credentials. Granting 'Read' permissions on a secret key that maps to database credentials for a specific team ensures that only members of that team can access Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from these credentials. This approach aligns with the principle of least privilege, granting users the minimum level of access required to perform their jobs, thus enhancing security.

질문 # 111
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