Free Databricks Certified-Data-Engineer-Professional Exam Dumps Questions & Answers

A healthcare analytics team is implementing a dimensional model in Delta Lake for patient care analysis. They have a date dimension table and are evaluating design options to ensure it supports a wide range of time-based analyses. Which design approach for the date dimension will support efficient time-based querying and aggregation?

• A.Store the date as string in ISO format (YYYY-MM-DD) for readability.
• B.Store only the date value and calculate all time attributes in queries.
• C.Pre-calculate attributes like fiscal_period, quarter, month_name, day_of_week, and holiday.
• D.Create separate dimension tables for different calendar systems (fiscal, academic, etc.)

The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:

The table daily_store_sales contains all the information needed to update store_sales_summary.
The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?

• A.Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
• B.Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
• C.Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
• D.Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
• E.Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.

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

A data engineer is optimizing a MERGE operation on an 800GB UC-managed table that experiences frequent updates and deletions. Which two actions should the engineer prioritize to improve MERGE performance? (Choose two.)

• A.Enable deletion vectors on the table if not already enabled.
• B.Use ZORDER on high-cardinality columns.
• C.Partition the table by date.
• D.Overwrite the table instead of Merge.
• E.Apply liquid clustering using the merge join keys.

A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
How should the data engineer fulfill these requirements?

• A.Use Auto Loader in batch mode with a daily job to overwrite the Delta table.
• B.Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
  "cloudFiles.schemaEvolutionMode"= "addNewColumns"
• C.Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"
• D.Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")