Databricks Databricks-Certified-Professional-Data-Engineer Zertifizierungsfragen - Databricks-Certified-Professional-Data-Engineer Prüfungsübungen

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

Section	Weight	Objectives
Data Modelling	6%	- Schema design and management - Delta Lake table design - Medallion Architecture implementation
Developing Code for Data Processing using Python and SQL	22%	- Batch and incremental processing logic - Data transformation and aggregation - Integration with Databricks APIs and tools
Monitoring and Alerting	10%	- Pipeline observability and logging - Performance and health monitoring - Setting up alerts and notifications
Data Sharing and Federation	5%	- Cross-workspace and cross-cloud access - Unity Catalog data sharing
Cost & Performance Optimisation	13%	- Cluster configuration and scaling - Query optimization and caching - Storage optimization (partitioning, Z-order, indexing)
Debugging and Deploying	10%	- Troubleshooting pipelines and errors - CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs
Ensuring Data Security and Compliance	10%	- Access control and permissions - Data encryption and masking - Compliance standards implementation
Data Transformation, Cleansing, and Quality	10%	- Data validation and quality checks - Standardization and normalization - Handling missing or inconsistent data
Data Governance	7%	- Data lineage and metadata tracking - Unity Catalog management - Policy enforcement
Data Ingestion & Acquisition	7%	- Auto Loader and streaming ingestion - Schema inference and evolution - Connecting to diverse data sources

>> Databricks Databricks-Certified-Professional-Data-Engineer Zertifizierungsfragen <<

Databricks-Certified-Professional-Data-Engineer Prüfungsübungen, Databricks-Certified-Professional-Data-Engineer Fragen Antworten

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Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q96-Q101):

96. Frage
A data architect is designing a Databricks solution to efficiently process data for different business requirements.
In which scenario should a data engineer use a materialized view compared to a streaming table ?

A. Processing high-volume, continuous clickstream data from a website to monitor user behavior in real- time.
B. Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
C. Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
D. Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.

Antwort: D

Begründung:
Materialized views in Databricks are optimized for precomputing and caching results of complex SQL queries, joins, and aggregations. They store query outputs physically and automatically refresh on a schedule or incremental change basis, drastically improving BI dashboard performance and reducing compute costs.
Conversely, streaming tables are designed for real-time data ingestion and processing , enabling event- driven analytics and low-latency use cases.
Databricks documentation explicitly recommends materialized views for analytical workloads with periodic updates and streaming tables for continuously updating sources. Therefore, the correct choice is C , where complex aggregations from large tables benefit most from materialized precomputation for fast reporting.

97. Frage
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non- overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late- arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A. await("event_time + '10 minutes'")
B. slidingWindow("event_time", "10 minutes")
C. awaitArrival("event_time", "10 minutes")
D. withWatermark("event_time", "10 minutes")
E. delayWrite("event_time", "10 minutes")

Antwort: D

Begründung:
The correct answer is A. withWatermark("event_time", "10 minutes"). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The other options are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. References:
* Watermarking: https://docs.databricks.com/spark/latest/structured-streaming/watermarks.html
* Windowed aggregations: https://docs.databricks.com/spark/latest/structured-streaming/window- operations.html

98. Frage
The business reporting team requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts, transforms, and loads the data for their pipeline runs in 10 minutes. Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A. Schedule a job to execute the pipeline once an hour on a new job cluster.
B. Schedule a job to execute the pipeline once an hour on a dedicated interactive cluster.
C. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
D. Configure a job that executes every time new data lands in a given directory.

Antwort: A

Begründung:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "Job clusters are created for a job run and terminate when the job completes." Exact extract: "All-purpose (interactive) clusters are intended for interactive development and collaboration."

99. Frage
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by thedatevariable:

Assume that the fieldscustomer_idandorder_idserve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
B. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
C. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
D. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
E. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.

Antwort: D

Begründung:
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation using mergeInto. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.

100. Frage
A data engineer is configuring a Databricks Asset Bundle to deploy a job with granular permissions. The requirements are:
* Grant the data-engineers group CAN_MANAGE access to the job.
* Ensure the auditors group can view the job but not modify or run it.
* Avoid granting unintended permissions to other users or groups.
How should the data engineer deploy the job while meeting the requirements?

A. resources:
jobs:
my-job:
name: data-pipeline
tasks: (...)
job_clusters: (...)
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
B. resources:
jobs:
my-job:
name: data-pipeline
tasks: (...)
job_clusters: (...)
permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
C. permissions:
- group_name: data-engineers
level: CAN_MANAGE
- group_name: auditors
level: CAN_VIEW
resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
D. resources:
jobs:
my-job:
name: data-pipeline
tasks: [...]
job_clusters: [...]
permissions:
- group_name: data-engineers
level: CAN_MANAGE
permissions:
- group_name: auditors
level: CAN_VIEW

Antwort: A

Begründung:
Databricks documents that resource-specific permissions for bundle resources can be defined under the resource itself, such as resources.jobs. < job > .permissions , using group_name and level . The documented syntax supports CAN_VIEW , CAN_MANAGE , and related permission levels, which matches option B. ( Databricks Documentation ) Option C is invalid because it repeats the permissions key incorrectly. Option D applies top-level permissions more broadly across bundle resources instead of scoping them specifically to the job, which does not best satisfy the "avoid unintended permissions" requirement. Option B is therefore the correct and properly scoped configuration. ( Databricks Documentation )
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101. Frage
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