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Databricks Certified Data Engineer Professional Sample Questions:

1. A large company seeks to implement a near real-time solution involving hundreds of pipelines with parallel updates of many tables with extremely high volume and high velocity data.
Which of the following solutions would you implement to achieve this requirement?

A) Use Databricks High Concurrency clusters, which leverage optimized cloud storage connections to maximize data throughput.
B) Store all tables in a single database to ensure that the Databricks Catalyst Metastore can load balance overall throughput.
C) Partition ingestion tables by a small time duration to allow for many data files to be written in parallel.
D) Isolate Delta Lake tables in their own storage containers to avoid API limits imposed by cloud vendors.
E) Configure Databricks to save all data to attached SSD volumes instead of object storage, increasing file I/O significantly.

2. 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
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from 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) 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.
B) 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.
C) 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.
D) 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.
E) 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.

3. The data engineering team maintains the following code:
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Assuming that this code produces logically correct results and the data in the source table has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A) The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.
B) A batch job will update the gold_customer_lifetime_sales_summary table, replacing only those rows that have different values than the current version of the table, using customer_id as the primary key.
C) The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.
D) An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.
E) An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.

4. The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A) preds.write.format("delta").save("/preds/churn_preds")
B)

C)

D) preds.write.mode("append").saveAsTable("churn_preds")
E)

5. Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets.
Which statement describes a limitation of Databricks Secrets?

A) Iterating through a stored secret and printing each character will display secret contents in plain text.
B) Because the SHA256 hash is used to obfuscate stored secrets, reversing this hash will display Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from the value in plain text.
C) Secrets are stored in an administrators-only table within the Hive Metastore; database administrators have permission to query this table by default.
D) The Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials.
E) Account administrators can see all secrets in plain text by logging on to the Databricks Accounts console.

Solutions: