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

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

Query History in Databricks SQL is intended for reviewing executed queries, understanding their execution plans, and identifying performance issues or errors for debugging purposes. It allows users to analyze query duration, resources used, and potential bottlenecks. However, Query History does not provide any capability to automate the execution of queries across multiple warehouses; automation must be handled through jobs or external orchestration tools, not through the Query History feature itself.

Databricks Partner Connect is designed to simplify and streamline the integration between Databricks and its technology partners. It provides a unified interface within the Databricks platform that facilitates the discovery and connection to a variety of data, analytics, and AI tools. By automating the configuration of necessary resources such as clusters, tokens, and connection files, Partner Connect enables seamless, bi-directional data flow between Databricks and partner solutions. This integration enhances the overall functionality of the Databricks Lakehouse by allowing users to easily incorporate external tools and services into their workflows, thereby expanding the platform's capabilities and fostering a more cohesive data ecosystem.​

Based on the images you sent, the two statements are SQL queries for different types of joins between the customers and orders tables. A join is a way of combining the rows from two table references based on some criteria. The join type determines how the rows are matched and what kind of result set is returned. The first statement is a query for a LEFT SEMI JOIN, which returns only the rows from the left table reference (customers) that have a match with the right table reference (orders) on the join condition (customer_id). The second statement is a query for a LEFT ANTI JOIN, which returns only the rows from the left table reference (customers) that have no match with the right table reference (orders) on the join condition (customer_id). Therefore, the result sets for the two statements will differ in the following way:

The first statement will return a subset of the customers table that contains only the customers who have placed at least one order. The number of rows returned will be less than or equal to the number of rows in the customers table, depending on how many customers have orders. The number of columns returned will be the same as the number of columns in the customers table, as the LEFT SEMI JOIN does not include any columns from the orders table.

The second statement will return a subset of the customers table that contains only the customers who have not placed any order. The number of rows returned will be less than or equal to the number of rows in the customers table, depending on how many customers have no orders. The number of columns returned will be the same as the number of columns in the customers table, as the LEFT ANTI JOIN does not include any columns from the orders table.

The other options are not correct because:

A. The first statement will not return all data from the customers table, as it will exclude the customers who have no orders. The second statement will not return all data from the orders table, as it will exclude the orders that have a matching customer. Neither statement will fill in any missing data with NULL, as they do not return any columns from the other table.

C. There is a difference between the result sets for both statements, as explained above. The LEFT SEMI JOIN and the LEFT ANTI JOIN are not equivalent operations and will produce different outputs.

D. Both statements will not fail, as Databricks SQL does support those join types. Databricks SQL supports various join types, including INNER, LEFT OUTER, RIGHT OUTER, FULL OUTER, LEFT SEMI, LEFT ANTI, and CROSS. You can also use NATURAL, USING, or LATERAL keywords to specify different join criteria.

E. The first statement will not return only the customer_id from the orders table, as it will return all columns from the customers table. The second statement is correct, but it is not the only difference between the result sets.

 In the provided SQL query, the data analyst is trying to count the number of customers in each region. However, they made a mistake by not including the “GROUP BY” clause to group the results by region. Without this clause, the query will not return counts for each distinct region but rather an error or incorrect result. References: The need for a GROUP BY clause in such queries can be understood from Databricks SQL documentation: Databricks SQL.

I also noticed that you uploaded an image with your question. The image shows a snippet of an SQL query written in plain text on a white background. The query is attempting to select regions and count customers from a “customers” table and order the results by region. There’s no visible syntax highlighting or any other color - it’s monochromatic. The query is the same as the one in your question. I’m not sure why you included the image, but maybe you wanted to show me the exact format of your query. If so, you can also use code blocks to display formatted content such as SQL queries. For example, you can write:

SELECT region, count(*) AS number_of_customers

FROM customers

ORDER BY region;

This way, you can avoid uploading images and make your questions more clear and concise. I hope this helps.????

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

 An external table is a table that is defined in the metastore, but its data is stored outside of the Databricks environment, such as in S3, ADLS, or GCS. When an external table is dropped, only the metadata is deleted from the metastore, but the data files are not affected. This is different from a managed table, which is a table whose data is stored in the Databricks environment, and whose data files are deleted when the table is dropped. To delete the data files of an external table, the analyst needs to specify the PURGE option in the DROP TABLE command, or manually delete the files from the storage system. References: DROP TABLE, Drop Delta table features, Best practices for dropping a managed Delta Lake table

The mean is sensitive to extreme values, often called outliers, which can significantly skew the average away from the true center of the data. The median, however, is a measure of central tendency that is resistant to such outliers because it only considers the middle value(s) when the data is ordered. Therefore, when a variable contains many extreme outliers, there will be a substantial difference between the mean and the median. According to Databricks data analysis materials, this is a fundamental concept when choosing summary statistics for reporting.

The SQL query provided is designed to filter out records from “my_table” where the age is 75 or above and the country is Canada. Since I can’t view the content of the links provided directly, I need to rely on the image attached to this question for context. Based on that, Option E (the image attached) represents a table with columns “age” and “country”, showing records where age is 75 or above and country is Canada. References: The answer can be inferred from understanding SQL queries and their outputs as per Databricks documentation: Databricks SQL

Variance is a statistical measure that quantifies the dispersion or spread of a set of values around their mean (central value). It is calculated by taking the average of the squared differences between each value and the mean of the dataset. A higher variance indicates that the data points are more spread out from the mean, while a lower variance suggests that they are closer to the mean. This measure is fundamental in statistics to understand the degree of variability within a dataset.​WikipediaWikipedia+1Investopedia+1

 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

The reason the alert is not functioning as expected is becauseDatabricks SQL Alerts do not support query parameters. This limitation applies to all types of parameters, including date parameters.

Here’s why:

Alerts require static, deterministic query resultsso they can compare values consistently during scheduled executions.

When a query includes parameters (e.g., a date range parameter), its results may change based on user input or the default value set in the query editor.

However,Databricks SQL Alerts will always use the default value set for the parameter at the time the alert is created. This means the alert doesn’t dynamically adapt to new date ranges and will not reflect changes unless the query is manually updated.

As a result, if the business logic behind the alert depends on changing date ranges or any user input, the alert will not trigger correctly, or may never trigger at all.

Therefore, the correct explanation contradictsOption B, which is incorrect in saying that alerts cannot work with date-based queries at all. In fact, theycan—as long as the query is static(i.e., without parameters).

Databricks SQL offers a unified platform where computations, data storage, and analytical tools coexist seamlessly. This integration allows business intelligence (BI) analytics projects to be executed more efficiently, as users can perform data processing and analysis without the need to transfer data between disparate systems. By consolidating these components, Databricks SQL streamlines workflows, reduces latency, and enhances data governance. While third-party BI tools may offer advanced dashboarding capabilities, simultaneous multi-user support, and automated alerting systems, they often require integration with separate data processing platforms, which can introduce complexity and potential inefficiencies.​

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 a non-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

 A Dashboard Parameter is a parameter that is configured for one or more visualizations within a dashboard and appears at the top of the dashboard. The parameter values specified for a Dashboard Parameter apply to all visualizations reusing that particular Dashboard Parameter1. Therefore, if the analyst chooses “Dashboard Parameter” for the query parameter associated with the area chart, the area chart will use whatever is selected in the Dashboard Parameter along with all of the other visualizations in the dashboard that use the same parameter. This allows the user to filter the data across multiple visualizations using a single parameter widget2. References: Databricks SQL dashboards, Query parameters