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

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 D is correct. A temporary view is session-scoped and does not store physical data. It is suitable when the relational object is only needed in the current session or query context and should not be available to others in other sessions. A regular view also avoids copying physical data, but it is persistent and can be shared beyond the current session. Official Databricks extract: a view is a “virtual table that has no physical data,” and temporary views are scoped to the notebook/script or query level and cannot be referenced outside that scope.

Option B is correct because spark.table( " sales " ) returns the named table as a Spark DataFrame, which the data engineering team can then test using PySpark. SELECT * FROM sales is SQL text, not a PySpark command by itself. spark.sql( " sales " ) is invalid because spark.sql expects a SQL statement, not just a table name. spark.delta.table is not the standard PySpark API for loading a table. Official Databricks extract: DataFrameReader.table “returns the specified table as a DataFrame,” and Databricks also lists spark.table as a Spark operation that returns a DataFrame.

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.

 The mean value of a variable is the average of all the values in a data set, calculated by dividing the sum of the values by the number of values. The median value of a variable is the middle value of the ordered data set, or the average of the middle two values if the data set has an even number of values. The mean value is sensitive to outliers, which are values that are very different from the rest of the data. Outliers can skew the mean value and make it less representative of the central tendency of the data. The median value is more robust to outliers, as it only depends on the middle values of the data. Therefore, when the variable contains a lot of extreme outliers, the mean value and the median value will be meaningfully different, as the mean value will be pulled towards the outliers, while the median value will remain close to the majority of the data1. References: Difference Between Mean and Median in Statistics (With Example) - BYJU’S

Option B is correct. In the older Databricks SQL UI terminology used by this question, permissions for databases, tables, and views are managed from the Data area. In the current Databricks UI, this is handled through Catalog Explorer, where an admin or data owner opens the object, goes to the Permissions tab, clicks Grant, selects users or groups, and grants the required privileges. Dashboards are for reporting, SQL Warehouses provide compute, and Settings is not the object-level permission management area. Official Databricks documentation says privileges can be managed using SQL commands, CLI, Terraform, or Catalog Explorer, and the UI flow is: open the table details page, go to Permissions, click Grant, select users or groups, and assign privileges.

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.

Option B is correct. The program can be scheduled to run daily as a Databricks job, and Python control flow can determine whether the final query runs only when the current day is Sunday. Databricks jobs support scheduled execution, and PySpark notebooks can run SQL through Spark while using Python logic around those commands. Official Databricks extract: jobs can be configured “to run it on a time-based schedule,” and spark.sql/Spark DataFrame operations allow Python-based workflows to interact with SQL data.

A user-defined function (UDF) is a function defined by a user, allowing custom logic to be reused in the user environment1. To apply a UDF to a table, the syntax is SELECT udf_name(column_name) AS alias FROM table_name2. Therefore, option E is the correct way to use the UDF price to create a new column customer_price based on the existing columns customer_spend and customer_units from the table customer_summary. References:

What are user-defined functions (UDFs)?

User-defined scalar functions - SQL

V

Option B is correct after correcting the uploaded typo withcolumn to withColumn. A 10% tax is calculated by multiplying salary by 0.1, not by 10. The age column should be removed using .drop( " age " ). Options A and D multiply salary by 10, which calculates 1000%, not 10%. Options A and C also use dropField, which is not the correct DataFrame method for removing a top-level column. Official Databricks PySpark documentation states that withColumn adds or replaces a column and that drop(*cols) returns a new DataFrame without the specified columns.