Databricks-Certified-Data-Engineer-Professional Latest Exam Online - Databricks-Certified-Data-Engineer-Professional Certification Test Questions

The Databricks Certified Data Engineer Professional Exam Databricks-Certified-Data-Engineer-Professional Questions lead to Databricks Databricks-Certified-Data-Engineer-Professional certification. The Databricks-Certified-Data-Engineer-Professional certification is for anyone new to the industry. Whether you have just graduated from college, making a career change, already working in the sector, or searching for new ways to progress, the Databricks Databricks-Certified-Data-Engineer-Professional Certification is ideal for you. If you want to appear in the Databricks-Certified-Data-Engineer-Professional test of Databricks Databricks-Certified-Data-Engineer-Professional certification, you should have basic hands-on experience.

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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity.
Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

A. "Can Manage" privileges on the required cluster
B. Cluster creation allowed. "Can Attach To" privileges on the required cluster
C. Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster
D. "Can Restart" privileges on the required cluster
E. Cluster creation allowed. "Can Restart" privileges on the required cluster

Answer: D

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/security/auth-authz/access-control/cluster-acl
https://docs.databricks.com/en/security/auth-authz/access-control/cluster-acl.html Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

NEW QUESTION # 82
A data engineer is designing a system leveraging Lakeflow Declarative Pipeline technology to process real-time truck telemetry data ingested from JSON files in S3 using Auto Loader. The data includes truck_id, timestamp, location, speed, and fuel_level. The system must support two use cases:
- Near-real-time monitoring of the latest location, speed, and
fuel_level per truck_id for the operations team.
- Daily aggregated reports of total distance traveled and average fuel
efficiency per truck_id for the management team.
Which approach should the data engineer use for streaming tables and materialized views in the Lakeflow Declarative Pipeline to meet these requirements?

A. Define a streaming table to ingest and store the raw telemetry data, and create a streaming table to compute the daily aggregated distance and fuel efficiency per truck_id reporting. Create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
B. Define a streaming table to ingest and store the raw telemetry data, and create a streaming table to incrementally compute the latest location, speed, and fuel_level per truck_id for real-time monitoring. Create a materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
C. Define a streaming table to ingest and store the raw telemetry data, and create a materialized view to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.
Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.
D. Define a materialized view to ingest and store the raw telemetry data, and create a streaming table to compute the latest location, speed, and fuel_level per truck_id for real-time monitoring.Create another materialized view to compute the daily aggregated distance and fuel efficiency per truck_id for reporting.

Answer: B

Explanation:
A streaming table is the right construct to ingest continuously arriving telemetry from Auto Loader.
Computing the latest per truck_id requires near-real-time incremental updates as new events arrive, which is best handled with a downstream streaming table. The daily aggregates are naturally suited to a materialized view, which maintains precomputed results for reporting and refreshes efficiently without requiring a continuously running streaming aggregation for a once- per-day consumption pattern.

NEW QUESTION # 83
What statement is true regarding the retention of job run history?

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

Answer: A

NEW QUESTION # 84
A data engineer is creating a daily reporting job. There are two reporting notebooks--one for weekdays and one for weekends. An "if/else condition" task is configured as
{{job.start_time.is_weekday}} == true to route the job to either the weekday or weekend notebook tasks. The same job would be used across multiple time zones. Which action should a senior data engineer take upon reviewing the job to merge or reject the pull request?

A. Reject, as they should use {{job.trigger_time.is_weekday}} instead.
B. Reject, as the {{job.start_time.is_weekday}} is for the UTC timezone.
C. Reject, as the {{job.start_time.is_weekday}} is not a valid value reference.
D. Merge, as the job configuration looks good.

Answer: B

Explanation:
Databricks parameter templates like {{job.start_time.is_weekday}} evaluate in UTC time by default, not in local workspace or regional time zones. Therefore, when jobs are configured to run across different time zones, relying on is_weekday using UTC may cause scheduling and task routing mismatches (for example, triggering the weekday notebook in one region while it's still the weekend locally).
Databricks recommends adjusting conditional logic or pipeline parameters explicitly to handle time zone conversions if business requirements depend on local times. Because the engineer's configuration does not account for this behavior, a senior data engineer should reject the pull request and suggest time-zone-aware logic before merging.

NEW QUESTION # 85
The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer's suggestion is correct?

A. Delta Lake statistics are only collected on the first 4 columns in a table.
B. Text data cannot be stored with Delta Lake.
C. Delta Lake statistics are not optimized for free text fields with high cardinality.
D. The Delta log creates a term matrix for free text fields to support selective filtering.
E. ZORDER ON review will need to be run to see performance gains.

Answer: C

Explanation:
Converting the data to Delta Lake may not improve query performance on free text fields with high cardinality, such as the review column. This is because Delta Lake collects statistics on the minimum and maximum values of each column, which are not very useful for filtering or skipping data on free text fields. Moreover, Delta Lake collects statistics on the first 32 columns by default, which may not include the review column if the table has more columns. Therefore, the junior data engineer's suggestion is not correct. A better approach would be to use a full-text search engine, such as Elasticsearch, to index and query the review column. Alternatively, you can use natural language processing techniques, such as tokenization, stemming, and lemmatization, to preprocess the review column and create a new column with normalized terms that can be used for filtering or skipping data.

NEW QUESTION # 86
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