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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer needs to create an application that will collect information about the latest job run including the repair history. How should the data engineer format the request?

A) Call/api/2.1/jobs/runs/get with the run_id and include_history parameters
B) Call/api/2.1/jobs/runs/list with the job_id and include_history parameters
C) Call/api/2.1/jobs/runs/get with the job_id and include_history parameters
D) Call/api/2.1/jobs/runs/list with the run_id and include_history parameters

2. What statement is true regarding the retention of job run history?

A) It is retained for 90 days or until the run-id is re-used through custom run configuration
B) It is retained until you export or delete job run logs
C) It is retained for 60 days, during which you can export notebook run results to HTML
D) It is retained for 60 days, after which logs are archived
E) It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3

3. 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) 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.
B) Use ignoreChanges flag while streaming from customer_dim to avoid breaking the pipeline during updates and deletes.
C) Streaming from customer_dim table would only be possible in the case of SCD 2 retention.
D) Read change data feed from customer_dim table and apply filters to incrementally act on the change events.

4. A senior data engineer is planning large-scale data workflows. The current task is to identify the considerations that form a foundation for creating scalable data models that are essential for effective management of large datasets. The data engineering team has identified the core capabilities as part of a scalable data model to build a modern data platform and provided their reasoning for considering Delta Lake for review. The senior data engineer is responsible for identifying the recommendations that are not valid. Which key features can be ignored while evaluating Delta Lake?

A) Delta Lake's capability to process data in both batch and streaming modes seamlessly, providing flexibility in data ingestion and processing.
B) Delta Lake optimizes metadata handling, efficiently managing billions of files and facilitating scalability to petabyte-scale datasets.
C) Delta Lake works with various data formats (e.g., Parquet, JSON, CSV) and integrates well with Spark and Databricks tools.
D) Delta Lake provides limited support for monitoring and troubleshooting data pipelines, so relevant partner tools have to be identified and set up for enhanced operational efficiency.

5. Which statement regarding spark configuration on the Databricks platform is true?

A) Spark configuration set within an notebook will affect all SparkSession attached to the same interactive cluster
B) Spark configuration properties can only be set for an interactive cluster by creating a global init script.
C) When the same spar configuration property is set for an interactive to the same interactive cluster.
D) The Databricks REST API can be used to modify the Spark configuration properties for an interactive cluster without interrupting jobs.
E) Spark configuration properties set for an interactive cluster with the Clusters UI will impact all notebooks attached to that cluster.

Solutions: