Pass the Databricks Data Analyst Databricks-Certified-Data-Analyst-Associate Questions and answers with Dumpstech

Option B is correct. The statement explicitly sets the schema/database location to dbfs:/accounting/data. In Databricks SQL, CREATE SCHEMA supports a LOCATION schema_directory, and the documentation explains that the schema directory is the filesystem path where the schema is created. Because the user supplied LOCATION ' dbfs:/accounting/data ' , Databricks uses that path instead of the default warehouse directory. Option D would apply only when no custom location is supplied. References: Databricks CREATE SCHEMA documentation.

The correct answer is C because Databricks SQL Editor is used to create and save SQL queries. A saved query can later be opened, reused, shared, visualized, and added to a dashboard. A dashboard is not the saved SQL statement itself; it is a reporting object that can contain query results and visualizations. Query History stores past executions, but it is not the correct object for saving a reusable SQL statement.

Official documentation extract used: Databricks states that the SQL editor can be used to “author queries” and “create visualizations.” It also states that the Save button “saves your query,” and query results can be added to a dashboard.

According to the Databricks SQL documentation, when a data analyst clicks on the “Add visualization” button on the SQL Editor page, the default visualization type is Bar Chart. This is because the result set has two columns: one of type STRING and one of type INT. The Bar Chart visualization automatically assigns the STRING column to the X-axis and the INT column to the Y-axis. The Bar Chart visualization is suitable for showing the distribution of a numeric variable across different categories. References: Visualization in Databricks SQL, Visualization types

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.

A Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables every minute requires a high level of compute resources to handle the frequent data ingestion, processing, and writing. This could result in a significant cost for the organization, especially if the data volume and velocity are large. Therefore, the data analyst should share this caution with the project stakeholders before setting up the dashboard and evaluate the trade-offs between the desired refresh rate and the available budget. The other options are not valid cautions because:

B. The gold-level tables are assumed to be appropriately clean for business reporting, as they are the final output of the data engineering pipeline. If the data quality is not satisfactory, the issue should be addressed at the source or silver level, not at the gold level.

C. The streaming data is an appropriate data source for a dashboard, as it can provide near real-time insights and analytics for the business users. Structured Streaming supports various sources and sinks for streaming data, including Delta Lake, which can enable both batch and streaming queries on the same data.

D. The streaming cluster is fault tolerant, as Structured Streaming provides end-to-end exactly-once fault-tolerance guarantees through checkpointing and write-ahead logs. If a query fails, it can be restarted from the last checkpoint and resume processing.

E. The dashboard can be refreshed within one minute or less of new data becoming available in the gold-level tables, as Structured Streaming can trigger micro-batches as fast as possible (every few seconds) and update the results incrementally. However, this may not be necessary or optimal for the business use case, as it could cause frequent changes in the dashboard and consume more resources. References: Streaming on Databricks, Monitoring Structured Streaming queries on Databricks, A look at the new Structured Streaming UI in Apache Spark 3.0, Run your first Structured Streaming workload

The result set provided shows a combination of grouping by two columns (group_1 and group_2) with subtotals for each level of grouping and a grand total. This pattern is typical of a GROUP BY ... WITH ROLLUP operation in SQL, which provides subtotal rows and a grand total row in the result set.

Considering the query options:

A) Option A: GROUP BY group_1, group_2 INCLUDING NULL - This is not a standard SQL clause and would not result in subtotals and a grand total.

B) Option B: GROUP BY group_1, group_2 WITH ROLLUP - This would create subtotals for each unique group_1, each combination of group_1 and group_2, and a grand total, which matches the result set provided.

C) Option C: GROUP BY group_1, group 2 - This is a simple GROUP BY and would not include subtotals or a grand total.

D) Option D: GROUP BY group_1, group_2, (group_1, group_2) - This syntax is not standard and would likely result in an error or be interpreted as a simple GROUP BY, not providing the subtotals and grand total.

E) Option E: GROUP BY group_1, group_2 WITH CUBE - The WITH CUBE operation produces subtotals for all combinations of the selected columns and a grand total, which is more than what is shown in the result set.

The correct answer is Option B, which uses WITH ROLLUP to generate the subtotals for each level of grouping as well as a grand total. This matches the result set where we have subtotals for each group_1, each combination of group_1 and group_2, and the grand total where both group_1 and group_2 are NULL.

Option D is correct. Customer data is stored in the customer’s cloud account, such as object storage controlled by the customer. Databricks control-plane services manage platform components, while the customer’s data resides in the customer-controlled storage layer. Official Databricks extract: “Databricks doesn’t store your primary customer data. That lives in Amazon S3 or other systems you control.”

 Descriptive statistics is a branch of statistics that uses summary statistics, such as mean, median, mode, standard deviation, range, frequency, or correlation, to quantitatively describe and summarize data. Descriptive statistics can help data analysts understand the main features of a data set, such as its central tendency, variability, or distribution. Descriptive statistics can also help data analysts visualize data using charts, graphs, or tables. Descriptive statistics do not make any inferences or predictions about the data, unlike inferential statistics, which use data analysis techniques to infer properties of an underlying population or probability distribution from a sample of data. References: Databricks - Descriptive Statistics, Databricks - Data Analysis with Databricks SQL

the accounts.customers table is an EXTERNAL table, which means that it is stored outside the default warehouse directory and is not managed by Databricks. Therefore, when you run the DROP command on this table, it only removes the metadata information from the metastore, but does not delete the actual data files from the file system. This means that you can still access the data using the location path (dbfs:/stakeholders/customers) or create another table pointing to the same location. However, if you try to query the table using its name (accounts.customers), you will get an error because the table no longer exists in the metastore. References: DROP TABLE | Databricks on AWS, Best practices for dropping a managed Delta Lake table - Databricks

This statement is incorrect. In Databricks SQL, any user with sufficient permissions on the query or dashboard can configure a refresh schedule—workspace administrator privileges are not required.

Here is the breakdown of the correct information:

A. True – Queries can be scheduled to refresh at intervals ranging from 1 minute to 2 weeks.

B. True – You can configure refresh schedules in the Query Editor.

C. False statement – A query being refreshed does use a SQL Warehouse. However, the option in question says it does not use a warehouse, which would be incorrect in a different context. Since this is a trickier one, we know that scheduled queries do require a SQL Warehouse to run.

D. True – Refresh schedules are different from alerts; alerts are triggered based on specific conditions being met in query results.

E. False (and thus the correct answer to this question) – You do not need to be a workspace admin to set a refresh schedule. You only need the correct permissions on the object.