Free Databricks Databricks-Certified-Data-Engineer-Associate Practice Test & Real Exam Questions

A data engineer works for an organization that must meet a stringent Service Level Agreement (SLA) that demands minimal runtime errors and high availability for its data processing pipelines. The data engineer wants to avoid the operational overhead of managing and tuning clusters.
Which architectural solution will meet the requirements?

A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

What is the primary function of the Silver layer in the Databricks medallion architecture?

A data engineer is troubleshooting two different pipeline failures:
Pipeline A fails with a java.lang.OutOfMemoryError immediately after display(df.collect()) is called on a 100 GB dataset.
Pipeline B fails during a wide transformation joining two large tables, with an ExecutorLostFailure message indicating executor-memory exhaustion during the shuffle.
Which action should the data engineer take to address these two issues?

A data engineer has configured a Lakeflow Job that runs daily to ingest customer transaction data from a legacy relational database. The extracted data must be written directly to a Unity Catalog table and be immediately queryable through SQL. The team must also preserve data lineage.
Which action enables this ingestion with direct landing in Unity Catalog, preserved lineage, and immediate SQL query capability?

What are the transformations typically included in building the Bronze layer?

What Databricks feature can be used to check the data sources and tables used in a workspace?

A Databricks workflow fails at the last stage due to an error in a notebook. This workflow runs daily. The data engineer fixes the mistake and wants to rerun the pipeline. This workflow is very costly and time-intensive to run.
Which action should the data engineer do in order to minimise downtime and cost?

A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when It is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which approach can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

Which compute option should be chosen in a scenario where small-scale ad hoc Python scripts need to be run at high frequency and should wind down quickly after these queries have finished running?

A data engineer is writing a script that is meant to ingest new data from cloud storage. In the event of the Schema change, the ingestion should fail. It should fail until the changes downstream source can be found and verified as intended changes.
Which command will meet the requirements?

A data engineer needs to ingest JSON change data from Salesforce into Unity Catalog-governed Delta tables using a low-code, fully managed experience.
Which Databricks capability should the data engineer use?

A data engineer needs to parse only png files in a directory that contains files with different suffixes. Which code should the data engineer use to achieve this task?