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

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.

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.

Descriptive statistics refer to statistical methods used to describe and summarize the basic features of data in a study. They provide simple summaries about the sample and the measures, often including metrics such as mean, median, mode, range, and standard deviation. Databricks learning materials highlight that descriptive statistics use summary statistics to quantitatively describe and summarize data, providing insight into data distributions without making inferences or predictions.

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.

Option D is correct. Databricks SQL alerts run a query on a schedule, evaluate a condition, and notify destinations when the condition is met. Since the requirement is notification through a messaging webhook, the alert should use a webhook alert destination. Official Databricks extract: alerts “run queries on a schedule and notify you when a condition that you define is met.” Databricks notification documentation also states that admins can configure alternate notification destinations using webhooks.

Databricks provides a user-friendly interface that allows data analysts to quickly upload small datasets, such as a 50-row CSV file, and create tables within Databricks SQL. The steps are as follows:​

Access the Data Upload Interface:

In the Databricks workspace, navigate to the sidebar and click on New > Add or upload data.​

Select Create or modify a table.​

Upload the CSV File:

Click on the browse button or drag and drop the CSV file directly onto the designated area.​

The interface supports uploading up to 10 files simultaneously, with a total size limit of 2 GB.​

Configure Table Settings:

After uploading, a preview of the data is displayed.​

Specify the table name, select the appropriate schema, and configure any additional settings as needed.​

Create the Table:

Once all configurations are set, click on the Create Table button to finalize the process.​

This method is efficient for quickly importing small datasets without the need for additional tools or complex configurations. Options B, C, and D involve more complex or manual processes that are unnecessary for this task.​

 Databricks SQL uses a query cache to store the results of queries that have been executed previously. This improves the performance and efficiency of repeated queries. To determine if a query result was pulled from the cache, you can go to the Query History tab in the Databricks SQL UI and click on the text of the query. A slideout will appear on the right side of the screen, showing the query details, including the cache status. If the result came from the cache, the cache status will show “Cached”. If the result did not come from the cache, the cache status will show “Not cached”. You can also see the cache hit ratio, which is the percentage of queries that were served from the cache. References: The answer can be verified from Databricks SQL documentation which provides information on how to use the query cache and how to check the cache status. Reference link: Databricks SQL - Query Cache

Option D is correct because the desired result needs one row per customer, so the query must aggregate orders by customer_name. It also needs the output column name number_of_orders, so the aggregate expression must be aliased. Databricks SQL documentation states that count “returns the number” of rows in a group, and the SELECT clause supports a column alias for an expression result. Therefore, count(order_id) AS number_of_orders with GROUP BY customer_name is the correct statement. Option B counts correctly but does not alias the output column as required. Option A does not aggregate. Option C uses invalid SQL syntax because USE customer_name is not a grouping clause. References: Databricks count aggregate function and SELECT clause documentation.

The correct answer is C because a dashboard is designed to collect multiple visualizations and query outputs in one shared reporting interface. A SQL warehouse provides compute for running SQL queries, but it is not a sharing/reporting interface. Query History only shows past query executions. A visualization tab belongs to one query result, while a dashboard can display multiple results together for stakeholders.

Official documentation extract used: Databricks states that dashboards are used to “build data visualizations and share reports with your team.”