Vce Databricks Databricks-Certified-Data-Engineer-Professional Exam | New Databricks-Certified-Data-Engineer-Professional Exam Papers

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

Section	Weight	Objectives
Data Sharing and Federation	5%	- Implement Lakehouse Federation - Use Delta Sharing for secure data sharing - Manage cross-platform data access
Monitoring and Alerting	10%	- Track data lineage and metrics - Set up alerts and notifications - Monitor pipeline performance and health
Data Ingestion & Acquisition	7%	- Use Auto Loader and structured streaming - Ingest data from diverse sources - Handle incremental and batch data loads
Debugging and Deploying	10%	- Deploy using Asset Bundles, CLI, and APIs - Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines
Developing Code for Data Processing using Python and SQL	22%	- Write efficient and maintainable code - Implement complex data processing logic - Use Databricks-specific libraries and APIs
Ensuring Data Security and Compliance	10%	- Secure data at rest and in transit - Implement access control and permissions - Ensure data privacy and compliance
Data Modelling	6%	- Design Medallion Architecture - Optimize table design and partitioning - Implement dimensional and relational models
Data Transformation, Cleansing, and Quality	10%	- Implement schema evolution and management - Apply data cleansing and validation rules - Enforce data quality standards
Cost & Performance Optimisation	13%	- Apply cost management best practices - Optimize compute and storage resources - Improve query and pipeline performance
Data Governance	7%	- Enforce data policies and standards - Manage data assets and metadata - Use Unity Catalog for governance

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q168-Q173):

NEW QUESTION # 168
An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

A. Give the analytics team direct access to the production table.
B. Deep clone the table for the analytics team.
C. Create a new table for the analytics team using a CTAS statement.
D. Shallow clone the table for the analytics team.

Answer: D

Explanation:
A shallow clone of the production Delta table creates an instantaneous snapshot that references the same data files, so it introduces virtually no downtime or storage overhead and avoids interfering with the ongoing ETL. A deep clone would copy all data (very expensive and slow for
20B rows). CTAS rewrites data and is unnecessary; direct access to prod risks contention and accidental changes.

NEW QUESTION # 169
A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table
Which of the following describes how results are generated each time the dashboard is updated?

A. The total count of records is calculated from the Hive metastore
B. The total count of rows is calculated by scanning all data files
C. The total count of records is calculated from the parquet file metadata
D. The total count of rows will be returned from cached results unless REFRESH is run
E. The total count of records is calculated from the Delta transaction logs

Answer: E

Explanation:
Delta Lake maintains a transaction log that records details about every change made to a table.
When you execute a count operation on a Delta table, Delta Lake can use the information in the transaction log to calculate the total number of records without having to scan all the data files.
This is because the transaction log includes information about the number of records in each file, allowing for an efficient aggregation of these counts to get the total number of records in the table.

NEW QUESTION # 170
What statement is true regarding the retention of job run history?

A. It is retained for 60 days, after which logs are archived
B. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
C. It is retained for 90 days or until the run-id is re-used through custom run configuration
D. It is retained for 60 days, during which you can export notebook run results to HTML
E. It is retained until you export or delete job run logs

Answer: D

NEW QUESTION # 171
The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Which statement describes what the number alongside this field represents?

A. The globally unique ID of the newly triggered run.
B. The number of times the job definition has been run in the workspace.
C. The job_id is returned in this field.
D. The job_id and number of times the job has been are concatenated and returned.
E. The total number of jobs that have been run in the workspace.

Answer: A

Explanation:
When triggering a job run using the Databricks CLI, the run_id field in the response represents a globally unique identifier for that particular run of the job. This run_id is distinct from the job_id.
While the job_id identifies the job definition and is constant across all runs of that job, the run_id is unique to each execution and is used to track and query the status of that specific job run within the Databricks environment. This distinction allows users to manage and reference individual executions of a job directly.

NEW QUESTION # 172
The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load 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.

Answer: A

Explanation:
Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost-effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the cost, while also fulfilling the requirement for hourly data updates for the business reporting team's dashboards.

NEW QUESTION # 173
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