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Databricks Databricks-Certified-Data-Engineer-Professional Online Prüfung, Databricks-Certified-Data-Engineer-Professional Fragen&Antworten

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Databricks-Certified-Data-Engineer-Professional Fragen&Antworten & Databricks-Certified-Data-Engineer-Professional Probesfragen

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Databricks Certified Data Engineer Professional Exam Databricks-Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q172-Q177):

172. Frage
A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors
Which statement describes the results returned by this query?

A. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
B. All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
C. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.
D. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
E. All records from all columns will be displayed with the values in user_ltv.

Antwort: A

Begründung:
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.

173. Frage
A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?

A. Delete the failed run, disable the cluster policy, and re-execute all tasks.
B. Manually create a new cluster for Task Y, update the job configuration, and trigger a full re-run.
C. Edit the global cluster policy to allow the restricted instance type, then re-run the entire job.
D. Use "Repair run", override the cluster configuration for Task Y to use a permitted instance type, and let Databricks re-run only Task Y.

Antwort: D

Begründung:
Repair run allows re-running only the failed task without affecting successfully completed tasks.
Overriding the cluster configuration for the specific task resolves the policy violation quickly while keeping the rest of the job intact and minimizing disruption.

174. Frage
A task orchestrator has been configured to run two hourly tasks. First, an outside system writes Parquet data to a directory mounted at /mnt/raw_orders/. After this data is written, a Databricks job containing the following code is executed:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order, and that the time field indicates when the record was queued in the source system.
If the upstream system is known to occasionally enqueue duplicate entries for a single order hours apart, which statement is correct?

A. The orders table will contain only the most recent 2 hours of records and no duplicates will be present.
B. All records will be held in the state store for 2 hours before being deduplicated and committed to the orders table.
C. The orders table will not contain duplicates, but records arriving more than 2 hours late will be ignored and missing from the table.
D. Duplicate records arriving more than 2 hours apart will be dropped, but duplicates that arrive in the same batch may both be written to the orders table.
E. Duplicate records enqueued more than 2 hours apart may be retained and the orders table may contain duplicate records with the same customer_id and order_id.

Antwort: E

175. Frage
A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?

A. Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.
B. Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
C. Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
D. Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.
E. Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.

Antwort: B

Begründung:
https://www.databricks.com/blog/2017/05/22/running-streaming-jobs-day-10x-cost-savings.html

176. Frage
The data engineering team maintains the following code:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
B. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
C. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.
D. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
E. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.

Antwort: C

Begründung:
This is the correct answer because it describes what will occur when this code is executed. The Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from code uses three Delta Lake tables as input sources: accounts, orders, and order_items. These tables are joined together using SQL queries to create a view called new_enriched_itemized_orders_by_account, which contains information about each order item and its associated account details. Then, the code uses write.format("delta").mode("overwrite") to overwrite a target table called enriched_itemized_orders_by_account using the data from the view. This means that every time this code is executed, it will replace all existing data in the target table with new data based on the current valid version of data in each of the three input tables.

177. Frage
......

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