Databricks-Certified-Data-Analyst-Associate Exam Dumps - Databricks Certified Data Analyst Associate Exam

Option E is correct. A major benefit of open source technologies and open data formats is avoiding vendor lock-in. Databricks supports open formats and interfaces so data can be used across tools and systems rather than being locked into a proprietary platform. Cloud integrations, governance, and workload scalability are Databricks benefits, but the specific benefit of embracing open source is avoiding vendor lock-in. Official Databricks extract: “Using open data formats and interfaces helps to avoid” vendor lock-in, and Databricks states that no proprietary data formats are used because Delta Lake and Iceberg are open source.

Option D is correct because the table has zero real null values, but the result contains null values representing subtotal and grand-total rows. That behavior is produced by WITH CUBE, which creates aggregations for combinations of grouping columns, including (group_1, group_2), (group_1), (group_2), and the grand total (). The Databricks SQL documentation states that GROUP BY supports advanced aggregations through CUBE, and that CUBE is shorthand for grouping sets. Option A only returns detailed groups. Options B and C use invalid syntax in Databricks SQL. Reference: Databricks GROUP BY clause documentation.

Option C is correct because Auto Stop is specifically designed to stop a SQL warehouse when it has been idle for a configured number of minutes. This minimizes total warehouse running time after the scheduled dashboard refresh completes. Reducing cluster size may reduce hourly cost but does not stop the warehouse. Serverless can help with management, but the feature directly matching “only running when necessary” is Auto Stop. Official Databricks extract: “Auto Stop determines whether the warehouse stops if it’s idle for the specified number of minutes.” Databricks also notes that idle SQL warehouses continue to accumulate charges until stopped.

In this scenario, the data engineering team has configured a Structured Streaming pipeline that updates the gold-level tables every 10 minutes. To ensure that the dashboard reflects the most recent data, it is essential to set the dashboard ' s refresh schedule to an interval of 10 minutes or less. This synchronization ensures that stakeholders view the latest information shortly after it becomes available in the gold-level tables. Options B, C, and D do not directly address the requirement of aligning the dashboard refresh frequency with the data update interval.

The command INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers is not a valid syntax for inserting data into a table in Databricks SQL. According to the documentation12, the correct syntax for inserting data into a table is either:

INSERT { OVERWRITE | INTO } [ TABLE ] table_name [ PARTITION clause ] [ ( column_name [, ...] ) | BY NAME ] query

INSERT INTO [ TABLE ] table_name REPLACE WHERE predicate query

The command in the question is missing the OVERWRITE or INTO keyword, and the query part that specifies the source of the data to be inserted. The TABLE keyword is optional and can be omitted. The PARTITION clause and the column list are also optional and depend on the table schema and the data source. Therefore, the command in the question will fail with a syntax error.

INSERT | Databricks on AWS

INSERT - Azure Databricks - Databricks SQL | Microsoft Learn

Option C is correct. In Data Vault modeling, hubs represent core business entities, satellites store descriptive and historical attributes, and links represent relationships between hubs. A many-to-many relationship between business entities is therefore captured by a Link Table. The Databricks Data Analyst Associate Exam Guide includes Data Vault schemas as part of data modeling with Databricks SQL, and Databricks’ Data Vault guidance explains that links represent relationships between hub entities. References: Databricks Data Analyst Associate Exam Guide and Databricks Data Vault guidance.

Option C is correct. Different visualization types are used to communicate different insights. For example, a bar chart may compare categories, a line chart may show change over time, and a table may show detailed records. Databricks dashboard documentation confirms that dashboard visualizations can select datasets, choose visualization types, and define x-axis and y-axis fields. The official exam guide also includes identifying the effective visualization type to communicate insights clearly. Option A is false because dashboard visualizations can be configured independently. Option B is false because categorical data is commonly shown with bar charts, not line charts. Option D is false because bar charts and histograms serve different analytical purposes. References: Databricks dashboard visualization documentation and Data Analyst Associate Exam Guide.

The correct answer is C because axis settings are configured in the visualization editor, specifically under the Y-axis settings. The Query Editor is for writing SQL, not configuring chart axes. User Settings do not control individual chart scales. Dashboard editing may contain visualizations, but the specific control is in the visualization editor’s Y Axis configuration.

Official documentation extract used: Databricks chart options state that to configure formatting for the X axis or Y axis, click the corresponding axis tab, and the Y-axis supports scale and range controls.