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

SectionObjectives
Topic 1: 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
        Topic 2: Data Governance and Security- Access control and permissions
        • 1. Unity Catalog basics
          • 2. Row-level and column-level security concepts
            Topic 3: Databricks SQL and SQL Analytics- Data transformation and analysis
            • 1. Common table expressions (CTEs)
              • 2. Window functions
                - Querying data using SQL in Databricks
                • 1. Joins and aggregations
                  • 2. SELECT statements and filtering data
                    Topic 4: Data Visualization and Dashboards- Creating dashboards in Databricks SQL
                    • 1. Dashboard configuration and sharing
                      • 2. Visualizing query results

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                        Databricks Certified Data Analyst Associate Exam Sample Questions (Q82-Q87):

                        NEW QUESTION # 82
                        Which example of data projects represents a common analytics application to be completed in Databricks SQL?

                        Answer: B

                        Explanation:
                        Option B is correct. A data analyst commonly uses Databricks SQL for analytics-focused transformations, aggregation, joining, filtering, and preparation of datasets for reporting or dashboarding. Analysis-specific ETL on Gold-layer tables is a "last-mile" analytics task: the Gold layer is already curated for business use, and the analyst performs additional project-specific refinement. The official Databricks exam guide includes creating views, performing aggregate operations, combining tables with joins, filtering, sorting, and using dashboards as Data Analyst Associate skills. It also states that Gold-layer data drives downstream analytics and dashboards. Options A, C, and D are primarily streaming engineering, machine learning, and MLOps activities, not common Databricks SQL analytics applications. References: Databricks Certified Data Analyst Associate Exam Guide and medallion architecture documentation.


                        NEW QUESTION # 83
                        Which of the following is stored in the Databricks customer's cloud account?

                        Answer: B

                        Explanation:
                        Option D is correct. Customer data is stored in the customer's cloud account, such as object storage controlled by the customer. Databricks control-plane services manage platform components, while the customer's data resides in the customer-controlled storage layer. Official Databricks extract: "Databricks doesn't store your primary customer data. That lives in Amazon S3 or other systems you control."


                        NEW QUESTION # 84
                        A data organization has a team of engineers developing data pipelines following the medallion architecture using Delta Live Tables. While the data analysis team working on a project is using gold-layer tables from these pipelines, they need to perform some additional processing of these tables prior to performing their analysis.
                        Which of the following terms is used to describe this type of work?

                        Answer: B

                        Explanation:
                        Last-mile ETL is the term used to describe the additional processing of data that is done by data analysts or data scientists after the data has been ingested, transformed, and stored in the lakehouse by data engineers. Last-mile ETL typically involves tasks such as data cleansing, data enrichment, data aggregation, data filtering, or data sampling that are specific to the analysis or machine learning use case. Last-mile ETL can be done using Databricks SQL, Databricks notebooks, or Databricks Machine Learning. Reference: Databricks - Last-mile ETL, Databricks - Data Analysis with Databricks SQL


                        NEW QUESTION # 85
                        A data analyst has produced a visualization. A stakeholder has viewed the visualization and is complaining that the visualization is difficult to interpret. After looking at the visualization, the analyst determines that the scale of the y-axis must be changed.
                        Where are the controls for changing the scale of the y-axis in Databricks SQL?

                        Answer: D

                        Explanation:
                        The correct answer is C because axis settings are configured in the visualization editor, specifically under the Y-axis settings. The Query Editor is for writing SQL, not configuring chart axes. User Settings do not control individual chart scales. Dashboard editing may contain visualizations, but the specific control is in the visualization editor's Y Axis configuration.
                        Official documentation extract used: Databricks chart options state that to configure formatting for the X axis or Y axis, click the corresponding axis tab, and the Y-axis supports scale and range controls.


                        NEW QUESTION # 86
                        A data analyst wants the following output:

                        Which statement will produce this output?

                        Answer: A

                        Explanation:
                        Option D is correct because the desired result needs one row per customer, so the query must aggregate orders by customer_name. It also needs the output column name number_of_orders, so the aggregate expression must be aliased. Databricks SQL documentation states that count "returns the number" of rows in a group, and the SELECT clause supports a column alias for an expression result. Therefore, count(order_id) AS number_of_orders with GROUP BY customer_name is the correct statement. Option B counts correctly but does not alias the output column as required. Option A does not aggregate. Option C uses invalid SQL syntax because USE customer_name is not a grouping clause. References: Databricks count aggregate function and SELECT clause documentation.


                        NEW QUESTION # 87
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