Databricks-Certified-Data-Analyst-Associate Databricks Certified Data Analyst Associate Exam Question and Answers

The Owner field in the table page shows the current owner of the table and allows the owner to change it to another user or group. To change the ownership of the table, the owner can click on the Owner field and select the new owner from the drop-down list. This will transfer the ownership of the table to the selected user or group and remove the previous owner from the list of table access control entries1. The other options are incorrect because:

• A. Removing the owner’s account from the Owner field will not change the ownership of the table, but will make the table ownerless2.
• B. Selecting All Users from the Owner field will not change the ownership of the table, but will grant all users access to the table3.
• D. Selecting the Admins group from the Owner field will not change the ownership of the table, but will grant the Admins group access to the table3.
• E. Removing all access from the Owner field will not change the ownership of the table, but will revoke all access to the table4. References:
• 1: Change table ownership
• 2: Ownerless tables
• 3: Table access control
• 4: Revoke access to a table

 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 verydifferent 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

Refresh schedules are used to rerun queries at specified intervals, and these queries typically require computational resources to execute. In the context of a cloud data service like Databricks, this would typically involve the use of a SQL Warehouse (or a SQL Endpoint, as they were formerly known) to provide the necessary computational resources. Therefore, the statement is incorrect because scheduled query refreshes would indeed use a SQL Warehouse/Endpoint to execute the query.

Databricks SQL uses ANSI SQL as its standard SQL dialect, which means it follows the SQL specifications defined by the American National Standards Institute (ANSI). This makes it easier to migrate existing SQL queries from other data warehouses or platforms that also use ANSI SQL or a similar dialect, such as PostgreSQL, Oracle, or Teradata. By using ANSI SQL, Databricks SQL avoids surprises in behavior or unfamiliar syntax that may arise from using anon-standard SQL dialect, such as Spark SQL or Hive SQL12. Moreover, Databricks SQL also adds compatibility features to support common SQL constructs that are widely used in other data warehouses, such as QUALIFY, FILTER, and user-defined functions2. References: ANSI compliance in Databricks Runtime, Evolution of the SQL language at Databricks: ANSI standard by default and easier migrations from data warehouses

Databricks SQL is not meant to replace or substitute other BI tools, but rather to complement them by providing a fast and easy way to query, explore, and visualize data on the lakehouse using the built-in SQL editor, visualizations, and dashboards. Databricks SQL also integrates seamlessly with popular BI tools like Tableau, Power BI, and Looker, allowing analysts to use their preferred tools to access data through Databricks clusters and SQL warehouses. Databricks SQL offers low-code and no-code experiences, as well as optimized connectors and serverless compute, to enhance the productivity and performance of BI workloads on the lakehouse. References: Databricks SQL, Connecting Applications and BI Tools to Databricks SQL, Databricks integrations overview, Databricks SQL: Delivering a Production SQL Development Experience on the Lakehouse

Higher-order functions are a simple extension to SQL to manipulate nested data such as arrays. A higher-order function takes an array, implements how the array is processed, and what the result of the computation will be. It delegates to a lambda function how to process each item in the array. This allows you to define functions that manipulate arrays in SQL, without having to unpack and repack them, use UDFs, or rely on limited built-in functions. Higher-order functions provide a performance benefit over user defined functions. References: Higher-order functions | Databricks on AWS, Working with Nested Data Using Higher Order Functions in SQL on Databricks | Databricks Blog, Higher-order functions - Azure Databricks | Microsoft Learn, Optimization recommendations on Databricks | Databricks on AWS

 Databricks SQL is a service that allows users to query data in the lakehouse using SQL and create visualizations and dashboards1. One of the common use cases for Databricks SQL is to combine data from different sources and formats into a single, comprehensive dataset that can be used for further analysis or reporting2. For example, a data analyst can use Databricks SQL to join data from a CSV file and a Parquet file, or from a Delta table and a JDBC table, and create a new table or view that contains the combined data3. This can help simplify the data management and governance, as well as improve the data quality and consistency. References:

• Databricks SQL overview
• Databricks SQL use cases
• Joining data sources

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 result set provided shows a combination of grouping by two columns (group_1andgroup_2) with subtotals for each level of grouping and a grand total. This pattern is typical of aGROUP BY ... WITH ROLLUPoperation 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 uniquegroup_1, each combination ofgroup_1andgroup_2, and a grand total, which matches the result set provided.

C)Option C:GROUP BY group_1, group 2- This is a simpleGROUP BYand 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 simpleGROUP BY, not providing the subtotals and grand total.

E)Option E:GROUP BY group_1, group_2 WITH CUBE- TheWITH CUBEoperation 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 isOption B, which usesWITH ROLLUPto generate the subtotals for each level of grouping as well as a grand total. This matches the result set where we have subtotals for eachgroup_1, each combination ofgroup_1andgroup_2, and the grand total where bothgroup_1andgroup_2areNULL.

Delta Lake stores table data as a series of data files in a specified location, but it also stores table metadata in a transaction log. The table metadata includes the schema, partitioning information, table properties, and other configuration details. The table metadata is stored alongside the data files and is updated atomically with every write operation. The table metadata can be accessed using the DESCRIBE DETAIL command or the DeltaTable class in Scala, Python, or Java. The table metadata can also be enriched with custom tags or user-defined commit messages using the TBLPROPERTIES or userMetadata options. References:

• Enrich Delta Lake tables with custom metadata
• Delta Lake Table metadata - Stack Overflow
• Metadata - The Internals of Delta Lake