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

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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is building a customer data pipeline in Lakeflow Spark Declarative Pipelines. The source is a cloud-based event stream with limited retention containing inserts, updates, and deletes for customer records. These changes are being applied using the AUTO CDC INTO syntax to maintain an SCD Type 1 table as the target table, customer_dim. How should the data engineer build a downstream job that streams from the customer_dim table to only act on updates and delete events, processing data incrementally?

A) Use ignoreChanges flag while streaming from customer_dim to avoid breaking the pipeline during updates and deletes.
B) Streaming from customer_dim table would only be possible in the case of SCD 2 retention.
C) When stored as SCD 1, the target of AUTO CDC INTO includes updates and deletes. Streaming from customer_dim can fail due to these operations. Instead, build another stream from the original source.
D) Read change data feed from customer_dim table and apply filters to incrementally act on the change events.

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

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

3. A table in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

A) Modify the overwrite logic to include a field populated by calling
spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.
B) Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.
C) Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.
D) Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.
E) Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.

4. The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A) Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.
B) Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
C) Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
D) Instruct the marketing team to download results as a CSV and email them to the sales organization.
E) Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.

5. When monitoring a complex workload, being able to see the query plan is critical to understanding what the workload is doing. Where can the visualization of the query plan be found?

A) In the Query Profiler, under Query Source
B) In the Query Profiler, under the Stages tab
C) In the Spark UI, under the SQL/DataFrame tab
D) In the Spart UI, under the Jobs tab

Solutions: