Free Databricks Databricks-Certified-Data-Analyst-Associate Practice Exam with Questions & Answers | Set: 3

Option C is correct. If an analyst encounters PII that they should not access, they should stop using that data and escalate through the appropriate organizational process. They should not delete the table, drop the column, or silently continue. Databricks security and privacy guidance emphasizes least-privilege access, data classification by sensitivity, access control, and meeting compliance/privacy requirements, including redaction or obfuscation of PII when required. Therefore, notifying a supervisor or data owner is the correct professional and governance-aligned response. References: Databricks security, compliance, and privacy documentation.

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 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.

 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

Option E is correct. A Silver-to-Gold hop typically reads cleaned/refined Silver data and writes aggregated, analytics-ready Gold data. The query reads from sales, groups by store, and aggregates sum( " sales " ), producing a summary table suitable for reporting or dashboarding. That matches the Gold layer. Option A reads from a raw location, which is not Silver-to-Gold. Option D filters invalid units, which is a cleaning step associated with Silver. Options B and C add a derived column but do not create a Gold-level aggregated table. Official Databricks medallion architecture documentation states that Silver is where data cleanup and validation are performed, while the Gold layer “consists of aggregated data tailored for analytics and reporting.” Databricks Structured Streaming documentation also shows .writeStream.outputMode( " complete " ).toTable(...) as a valid output mode pattern for stateful streaming aggregations.

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.”

Option C is correct because the requirement is to run the SQL query and then operate on the results in PySpark. spark.sql(...) executes SQL text and returns a Spark DataFrame that can be used in PySpark tests. spark.table(...) can load a table directly, but it does not run an arbitrary SQL query. Option A is SQL text by itself, not a PySpark operation. Official Databricks documentation states that a SparkSession can be used to execute SQL over tables, and Databricks notebook documentation specifically references using SQL inside Python with a spark.sql command.

Databricks SQL uses SQL warehouses as its compute resource. A SQL warehouse is a dedicated compute engine designed specifically for executing SQL queries and powering dashboards within the Databricks workspace. According to Databricks official documentation, SQL warehouses are optimized for fast, scalable query execution, whereas clusters are used primarily for data engineering and machine learning workloads.

To share a Databricks SQL dashboard with stakeholders who do not have accounts in the Databricks deployment and ensure they are notified upon each refresh, the data analyst can add the stakeholders ' email addresses to the dashboard ' s refresh schedule subscribers list. This approach allows the stakeholders to receive email notifications containing the latest dashboard updates without requiring them to have direct access to the Databricks workspace. This method is efficient and minimizes effort, as it automates the notification process and ensures stakeholders remain informed of the most recent data insights.​

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.