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Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:

SectionObjectives
Data Visualization and Dashboards- Creating dashboards in Databricks SQL
  • 1. Dashboard configuration and sharing
    • 2. Visualizing query results
      Data Governance and Security- Access control and permissions
      • 1. Row-level and column-level security concepts
        • 2. Unity Catalog basics
          Databricks SQL and SQL Analytics- Querying data using SQL in Databricks
          • 1. SELECT statements and filtering data
            • 2. Joins and aggregations
              - Data transformation and analysis
              • 1. Window functions
                • 2. Common table expressions (CTEs)
                  Data Management in Lakehouse- Delta Lake fundamentals
                  • 1. ACID transactions and versioning
                    • 2. Table optimization concepts
                      - Data ingestion and preparation
                      • 1. Batch and streaming ingestion concepts

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                        Databricks Certified Data Analyst Associate Exam Sample Questions (Q50-Q55):

                        NEW QUESTION # 50
                        A data analyst wants the following output:
                        customer_name
                        number_of_orders
                        John Doe
                        388
                        Zhang San
                        234
                        Which statement will produce this output?

                        Answer: A

                        Explanation:
                        To get the number of orders per customer, you need to join the customers and orders tables on the customer_id, count the order_id, and group the results by customer_name. The correct SQL syntax, as outlined in Databricks SQL documentation, is to use GROUP BY on the selected customer field and use COUNT for aggregation. Only option A does this correctly, while the other options contain syntax errors or incorrect field names.


                        NEW QUESTION # 51
                        A table named user_ltv is being used to create a view that will be used by data analysts on various teams.
                        Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
                        The user_ltv table has the following schema:
                        email STRING, age INT, ltv INT
                        The following view definition is executed:
                        CREATE VIEW user_ltv_no_minors AS
                        SELECT email, age, ltv
                        FROM user_ltv
                        WHERE
                        CASE
                        WHEN is_member( " auditing " ) THEN TRUE
                        ELSE age > = 18
                        END;
                        An analyst who is not a member of the auditing group executes the following query:
                        SELECT * FROM user_ltv_no_minors;
                        Which statement describes the results returned by this query?

                        Answer: D

                        Explanation:
                        Option A is correct. The user is not a member of the auditing group, so is_member( " auditing " ) evaluates to false and the ELSE age > = 18 branch controls the filter. Rows with age > = 18 are returned; rows under 18 are omitted. "Age greater than 17" is equivalent to age > = 18 for integer ages. Official Databricks extract:
                        is_member() "returns TRUE if the current user is a member" of the specified group, and Databricks describes dynamic views as views that can filter rows based on group membership.


                        NEW QUESTION # 52
                        Which statement describes descriptive statistics?

                        Answer: C


                        NEW QUESTION # 53
                        A data analyst has been asked to use the below table sales_table to get the percentage rank of products within region by the sales:

                        The result of the query should look like this:

                        Which of the following queries will accomplish this task?
                        A)

                        B)

                        C)

                        Answer: D

                        Explanation:
                        The correct query to get the percentage rank of products within region by the sales is option B. This query uses the PERCENT_RANK() window function to calculate the relative rank of each product within each region based on the sales amount. The window function is partitioned by region and ordered by sales in descending order. The result is aliased as rank and displayed along with the region and product columns. The other options are incorrect because:
                        A) Option A uses the RANK() window function instead of the PERCENT_RANK() function. The RANK() function returns the rank of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM().
                        C) Option C uses the DENSE_RANK() window function instead of the PERCENT_RANK() function. The DENSE_RANK() function returns the rank of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM().
                        D) Option D uses the ROW_NUMBER() window function instead of the PERCENT_RANK() function. The ROW_NUMBER() function returns the sequential number of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM(). Reference:
                        1: PERCENT_RANK (Transact-SQL)
                        2: Window functions in Databricks SQL
                        3: Databricks Certified Data Analyst Associate Exam Guide


                        NEW QUESTION # 54
                        A data analyst creates a Databricks SQL Query where the result set has the following schema:
                        region STRING
                        number_of_customer INT
                        When the analyst clicks on the " Add visualization " button on the SQL Editor page, which of the following types of visualizations will be selected by default?

                        Answer: A

                        Explanation:
                        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


                        NEW QUESTION # 55
                        ......

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