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Databricks Certified Data Engineer Professional Exam Sample Questions (Q74-Q79):

NEW QUESTION # 74
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. 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.
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. The orders table will contain only the most recent 2 hours of records and no duplicates will be present.
E. 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.

Answer: A

NEW QUESTION # 75
A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?

A. Define and unit test functions using Files in Repos
B. Define units test and functions within the same notebook
C. Define and import unit test functions from a separate Databricks notebook
D. Run unit tests against non-production data that closely mirrors production

Answer: D

Explanation:
The best practice for running unit tests on functions that interact with data is to use a dataset that closely mirrors the production data. This approach allows data engineers to validate the logic of their functions without the risk of affecting the actual production data. It's important to have a representative sample of production data to catch edge cases and ensure the functions will work correctly when used in a production environment.

NEW QUESTION # 76
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
B. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
C. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
D. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations

Answer: D

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade- off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job.

NEW QUESTION # 77
A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impression led to monitizable clicks.

Which solution would improve the performance?

Answer: A

Explanation:
When joining a stream of advertisement impressions with a stream of user clicks, you want to Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from minimize the state that you need to maintain for the join. Option A suggests using a left outer join with the condition that clickTime == impressionTime, which is suitable for correlating events that occur at the exact same time. However, in a real-world scenario, you would likely need some leeway to account for the delay between an impression and a possible click. It's important to design the join condition and the window of time considered to optimize performance while still capturing the relevant user interactions. In this case, having the watermark can help with state management and avoid state growing unbounded by discarding old state data that's unlikely to match with new data.

NEW QUESTION # 78
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Define a view against the products_per_order table and define the dashboard against this view.
B. Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.
C. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
D. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
E. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.

Answer: B

NEW QUESTION # 79
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DATABRICKS CERTIFIED DATA ENGINEER PROFESSIONAL EXAM TRAINING VCE - DATABRICKS-CERTIFIED-DATA-ENGINEER-PROFESSIONAL LAB QUESTIONS & DATABRICKS CERTIFIED DATA ENGINEER PROFESSIONAL EXAM PRACTICE TRAINING

Databricks Certified Data Engineer Professional Exam Training Vce - Databricks-Certified-Data-Engineer-Professional Lab Questions & Databricks Certified Data Engineer Professional Exam Practice Training