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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A) Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
B) Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.
C) Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
D) Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
E) Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.

2. A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

A) The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.
B) Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.
C) Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
D) Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
E) Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.

3. 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".

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)

B) preds.write.mode("append").saveAsTable("churn_preds")
C)

D)

E) preds.write.format("delta").save("/preds/churn_preds")

4. Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

A) spark.sql.files.maxPartitionBytes
B) spark.sql.adaptive.coalescePartitions.minPartitionNum
C) spark.sql.autoBroadcastJoinThreshold
D) spark.sql.files.openCostInBytes
E) spark.sql.adaptive.advisoryPartitionSizeInBytes

5. A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data. Which configuration will provide the optimal performance while enabling these capabilities?

A) Share tables WITH HISTORY, ensure tables don't have partitioning enabled, and enable CDF before sharing.
B) Share tables WITHOUT HISTORY and enable partitioning for better query performance.
C) Use the open sharing protocol instead of Databricks-to-Databricks sharing for better performance.
D) Share the entire schema WITHOUT HISTORY and rely on recipient-side caching for performance.

Solutions: