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Databricks Certified Professional Data Engineer Exam Sample Questions (Q93-Q98):

NEW QUESTION # 93
A data engineering team has created a series of tables using Parquet data stored in an external sys-tem. The
team is noticing that after appending new rows to the data in the external system, their queries within
Databricks are not returning the new rows. They identify the caching of the previous data as the cause of this
issue.
Which of the following approaches will ensure that the data returned by queries is always up-to-date?

A. The tables should be stored in a cloud-based external system
B. The tables should be updated before the next query is run
C. The tables should be converted to the Delta format
D. The tables should be refreshed in the writing cluster before the next query is run
E. The tables should be altered to include metadata to not cache

Answer: C

NEW QUESTION # 94
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 namedstore_saies_summaryand the schema is as follows:

The tabledaily_store_salescontains all the information needed to updatestore_sales_summary. The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
Ifdaily_store_salesis implemented as a Type 1 table and thetotal_salescolumn might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in thestore_sales_summary table?

A. 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.
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 batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
D. 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.
E. 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.

Answer: D

Explanation:
The daily_store_sales table contains all the information needed to update store_sales_summary. The schema of the table is:
store_id INT, sales_date DATE, total_sales FLOAT
The daily_store_sales table is implemented as a Type 1 table, which means that old values are overwritten by new values and no history is maintained. The total_sales column might be adjusted after manual data auditing, which means that the data in the table may change over time.
The safest approach to generate accurate reports in the store_sales_summary table is to 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. Structured Streaming is a scalable and fault-tolerant stream processing engine built on Spark SQL. Structured Streaming allows processing data streams as if they were tables or DataFrames, using familiar operations such as select, filter, groupBy, or join. Structured Streaming also supports output modes that specify how to write the results of a streaming query to a sink, such as append, update, or complete. Structured Streaming can handle both streaming and batch data sources in a unified manner.
The change data feed is a feature of Delta Lake that provides structured streaming sources that can subscribe to changes made to a Delta Lake table. The change data feed captures both data changes and schema changes as ordered events that can be processed by downstream applications or services. The change data feed can be configured with different options, such as starting from a specific version or timestamp, filtering by operation type or partition values, or excluding no-op changes.
By using Structured Streaming to subscribe to the change data feed for daily_store_sales, one can capture and process any changes made to the total_sales column due to manual data auditing. By applying these changes to the aggregates in the store_sales_summary table with each update, one can ensure that the reports are always consistent and accurate with the latest data. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Structured Streaming" section; Databricks Documentation, under "Delta Change Data Feed" section.

NEW QUESTION # 95
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. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
B. Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.
C. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
D. Define a view against the products_per_order table and define the dashboard against this view.

Answer: B

NEW QUESTION # 96
Which statement describes Delta Lake Auto Compaction?

A. 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.
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 128 MB.
C. 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.
D. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
E. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.

Answer: B

Explanation:
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. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Compaction for Delta Lake on Databricks" section.

NEW QUESTION # 97
When working with AUTO LOADER you noticed that most of the columns that were inferred as part of loading are string data types including columns that were supposed to be integers, how can we fix this?

A. Correct the incoming data by explicitly casting the data types
B. Provide the schema of the target table in the cloudfiles.schemalocation
C. Provide the schema of the source table in the cloudfiles.schemalocation
D. Update the checkpoint location
E. Provide schema hints

Answer: E

Explanation:
Explanation
The answer is, Provide schema hints.
1.spark.readStream
2.format("cloudFiles")
3.option("cloudFiles.format", "csv")
4.option("header", "true")
5.option("cloudFiles.schemaLocation", schema_location)
6.option("cloudFiles.schemaHints", "id int, description string")
7.load(raw_data_location)
8.writeStream
9.option("checkpointLocation", checkpoint_location)
10.start(target_delta_table_location)option("cloudFiles.schemaHints", "id int, description string")
# Here we are providing a hint that id column is int and the description is a string When cloudfiles.schemalocation is used to store the output of the schema inference during the load process, with schema hints you can enforce data types for known columns ahead of time.

NEW QUESTION # 98
......

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Karl Taylor Karl Taylor

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