Free Practice Questions for Databricks Databricks-Certified-Data-Engineer-Associate Exam

Cron syntax is a tool that can be used to represent and submit a complex run schedule programmatically. Cron syntax is a string of six fields that specify the frequency, date, and time of a job run. For example, the cron expression 0 0 12 * * ? means run the job at 12:00 PM every day. The data engineer can use the Databricks REST API to create or update a job with a cron schedule. The data engineer can also use the Databricks CLI to create or update a job with a cron schedule by using a JSON file that contains the cron expression. The other tools are either invalid or not suitable for representing and submitting a complex run schedule programmatically. References: Schedule a job, Jobs API, Databricks CLI, Cron expressions

Bronze tables are the first layer of a medallion architecture, which is a data design pattern used to organize data in a lakehouse. Bronze tables contain raw data ingested from various sources, such as RDBMS data, JSON files, IoT data, etc. The table structures in this layer correspond to the source system table structures “as-is”, along with any additional metadata columns that capture the load date/time, process ID, etc. The only transformation applied to the raw data in this layer is to apply a schema, which defines the column names and data types of the table. The schema can be inferred from the data source or specified explicitly. Applying a schema to the raw data enables the use of SQL and other structured query languages to access and analyze the data. Therefore, option E is the correct answer. References: What is a Medallion Architecture?, Raw Data Ingestion into Delta Lake Bronze tables using Azure Synapse Mapping Data Flow, Apache Spark + Delta Lake concepts, Delta Lake Architecture & Azure Databricks Workspace.

A left anti-join returns rows from the left-side relation for which no matching row exists in the right-side relation. By placing customers on the left and orders on the right and joining by customer_id, the result contains precisely the customers who have no corresponding order. A left anti-join also returns only columns from the left relation, which satisfies the requirement to exclude all order-related fields. A left outer join could produce the desired result only with an additional NULL filter and correct table order. An inner join returns customers who have placed orders, which is the opposite requirement. A full outer join can introduce order-only rows and unnecessary columns. Therefore, the left anti-join described in option C is the most exact and efficient expression for this requirement.

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When Automatically detect column types is disabled during file upload, Databricks does not infer specialized data types from the CSV values. Instead, every uploaded column is created as STRING. This behavior prevents values that resemble numbers, dates, or Boolean values from being automatically assigned potentially incorrect types. The engineer can subsequently transform or cast individual columns into the required target types as part of a controlled Silver-layer process. Option B is incorrect because partial numeric inference does not occur when automatic detection is disabled. Option C is incorrect because Databricks can create the table without manually supplied types by using STRING for every column. Option D is also incorrect because disabling detection determines the schema behavior for the table creation operation. Therefore, option A accurately describes the result.

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To send the Databricks Job owner an email in the case that the Job fails, the best approach is to set up an Alert in the Job page. This way, the Job owner can configure the email address and the notification type for the Job failure event. The other options are either not feasible, not reliable, or not relevant for this task. Manually programming an alert system in each cell of the Notebook is tedious and error-prone. Setting up an Alert in the Notebook is not possible, as Alerts are only available for Jobs and Clusters. There is a way to notify the Job owner in the case of Job failure, so option D is incorrect. MLflow Model Registry Webhooks are used for model lifecycle events, not Job events, so option E is not applicable. References:

Add email and system notifications for job events

Alerts

MLflow Model Registry Webhooks

A materialized view stores the result of its defining query and refreshes that result when required. This makes it appropriate for complex Gold-layer joins and aggregations that would otherwise be recomputed whenever a dashboard is opened. Because the source data changes only every few hours, the materialized view can be refreshed according to that update pattern while BI queries read precomputed results with lower latency and reduced resource consumption. A standard view stores only its query definition and executes the underlying computation when queried. A streaming table is intended primarily for incremental or continuously arriving data rather than periodic precomputation of BI aggregations. An external table describes where data is stored but does not inherently precompute query results. Therefore, option B best satisfies the performance and refresh requirements.

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