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

SectionObjectives
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. Common table expressions (CTEs)
        • 2. Window functions
          Data Management in Lakehouse- Data ingestion and preparation
          • 1. Batch and streaming ingestion concepts
            - Delta Lake fundamentals
            • 1. Table optimization concepts
              • 2. ACID transactions and versioning
                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. Unity Catalog basics
                      • 2. Row-level and column-level security concepts

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                        Databricks Certified Data Analyst Associate Exam Sample Questions (Q36-Q41):

                        NEW QUESTION # 36
                        What is used as a compute resource for Databricks SQL?

                        Answer: B

                        Explanation:
                        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.


                        NEW QUESTION # 37
                        A data analyst wants to create a dashboard with three main sections: Development, Testing, and Production. They want all three sections on the same dashboard, but they want to clearly designate the sections using text on the dashboard.
                        Which of the following tools can the data analyst use to designate the Development, Testing, and Production sections using text?

                        Answer: A

                        Explanation:
                        Markdown-based text boxes are useful as labels on a dashboard. They allow the data analyst to add text to a dashboard using the %md magic command in a notebook cell and then select the dashboard icon in the cell actions menu. The text can be formatted using markdown syntax and can include headings, lists, links, images, and more. The text boxes can be resized and moved around on the dashboard using the float layout option. Reference: Dashboards in notebooks, How to add text to a dashboard in Databricks


                        NEW QUESTION # 38
                        Which of the following statements about a refresh schedule is incorrect?

                        Answer: A

                        Explanation:
                        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.


                        NEW QUESTION # 39
                        A data analyst is working in an organization that utilizes a multi-hop, medallion architecture. They have been tasked with creating a new Gold table from an existing Silver table.
                        Which query is performing a hop from a Silver table to a Gold table?

                        Answer: A

                        Explanation:
                        Option C is the correct answer because it creates a new aggregated table, store_sales, from the existing cleaned
                        /refined table cleaned_transactions. In a medallion architecture, a table such as cleaned_transactions represents a Silver-layer table because it contains cleaned or validated transaction-level data. The query then creates store_sales by grouping by store_id and calculating SUM(sales), which is an aggregation suitable for analytics and reporting. That is exactly the Silver-to-Gold hop.
                        Option A creates cleaned_transactions from transactions, so it is moving toward a cleaned Silver table, not creating a Gold table. Option B creates transactions from raw_transactions, which is closer to a raw-to-Bronze or Bronze preparation step. Option D filters rows from transactions into cleaned_transactions, which is also a cleaning/validation step associated with Silver, not Gold.
                        Exact extract from official Databricks documentation: Databricks describes medallion architecture as improving data through "Bronze # Silver # Gold layer tables." The same official page identifies Silver as
                        "Data cleaning and validation" and Gold as "Dimensional modeling and aggregation." Databricks also states that the Gold layer "consists of aggregated data tailored for analytics and reporting."


                        NEW QUESTION # 40
                        What is a benefit of using Databricks SQL for business intelligence (Bl) analytics projects instead of using third-party Bl tools?

                        Answer: D

                        Explanation:
                        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.


                        NEW QUESTION # 41
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