Databricks-Certified-Data-Engineer-Professional Dumps Questions, Databricks-Certified-Data-Engineer-Professional Exam Dumps Pdf

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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q230-Q235):

NEW QUESTION # 230
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device. Bgm_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 pipelines, reset, allowed property to false on bpm_stats
B. Set the SkipChangeCommits flag to true raw_lot
C. Set the skipChangeCommits flag to true on bpm_stats
D. Set the pipelines, reset, allowed property to false on raw_iot

Answer: D

Explanation:
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.

NEW QUESTION # 231
A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

A. Date
B. latitude
C. Post_time
D. Post_id
E. User_id

Answer: A

Explanation:
Partitioning a Delta Lake table improves query performance by organizing data into partitions based on the values of a column. In the given schema, the date column is a good candidate for partitioning for several reasons:
Time-Based Queries: If queries frequently filter or group by date, partitioning by the date column can significantly improve performance by limiting the amount of data scanned. Granularity: The date column likely has a granularity that leads to a reasonable number of partitions (not too many and not too few). This balance is important for optimizing both read and write performance.
Data Skew: Other columns like post_id or user_id might lead to uneven partition sizes (data skew), which can negatively impact performance.
Partitioning by post_time could also be considered, but typically date is preferred due to its more manageable granularity.

NEW QUESTION # 232
To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.
B. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
C. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
D. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
E. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.

Answer: C

Explanation:
This is the correct answer because it addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed. The situation is that an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added, due to new requirements from a customer-facing application. By configuring a new table with all the requisite fields and new names and using this as the source for the customer-facing application, the data engineering team can meet the new requirements without affecting other teams that rely on the existing table schema and name. By creating a view that maintains the original data schema and Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from table name by aliasing select fields from the new table, the data engineering team can also avoid duplicating data or creating additional tables that need to be managed.

NEW QUESTION # 233
Which statement describes Delta Lake Auto Compaction?

A. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
B. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
C. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
D. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.
E. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.

Answer: D

Explanation:
This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones. Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB. Auto Compaction only compacts files that have not been compacted previously.

NEW QUESTION # 234
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
B. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
C. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
D. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
E. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.

Answer: E

Explanation:
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity.
Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs.

NEW QUESTION # 235
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