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

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

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                        Databricks Certified Data Analyst Associate Exam Sample Questions (Q80-Q85):

                        NEW QUESTION # 80
                        A data analyst has created a Query in Databricks SQL, and now wants to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
                        Which step will the data analyst need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?

                        Answer: D

                        Explanation:
                        Databricks SQL allows you to create multiple visualizations from a single query result. These visualizations can be customized independently and each can be added to a dashboard. This feature is explicitly supported and recommended in Databricks' documentation on dashboards and visualization workflows, enabling flexible reporting without duplicating queries.


                        NEW QUESTION # 81
                        Which of the following statements describes descriptive statistics?

                        Answer: A

                        Explanation:
                        Descriptive statistics is a branch of statistics that uses summary statistics, such as mean, median, mode, standard deviation, range, frequency, or correlation, to quantitatively describe and summarize data.
                        Descriptive statistics can help data analysts understand the main features of a data set, such as its central tendency, variability, or distribution. Descriptive statistics can also help data analysts visualize data using charts, graphs, or tables. Descriptive statistics do not make any inferences or predictions about the data, unlike inferential statistics, which use data analysis techniques to infer properties of an underlying population or probability distribution from a sample of data. References: Databricks - Descriptive Statistics, Databricks - Data Analysis with Databricks SQL


                        NEW QUESTION # 82
                        In which of the following situations will the mean value and median value of variable be meaningfully different?

                        Answer: C

                        Explanation:
                        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. Reference: Difference Between Mean and Median in Statistics (With Example) - BYJU'S


                        NEW QUESTION # 83
                        A data analyst has created a user-defined function using the following line of code:
                        CREATE FUNCTION price(spend DOUBLE, units DOUBLE)
                        RETURNS DOUBLE
                        RETURN spend / units;
                        Which of the following code blocks can be used to apply this function to the customer_spend and customer_units columns of the table customer_summary to create column customer_price?

                        Answer: C

                        Explanation:
                        A user-defined function (UDF) is a function defined by a user, allowing custom logic to be reused in the user environment1. To apply a UDF to a table, the syntax is SELECT udf_name(column_name) AS alias FROM table_name2. Therefore, option E is the correct way to use the UDF price to create a new column customer_price based on the existing columns customer_spend and customer_units from the table customer_summary. References:
                        * What are user-defined functions (UDFs)?
                        * User-defined scalar functions - SQL
                        V


                        NEW QUESTION # 84
                        A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every 10 minutes.
                        A data analyst has created a dashboard based on this gold level data. The project stakeholders want to see the results in the dashboard updated within 10 minutes or less of new data becoming available within the gold- level tables.
                        What is the ability to ensure the streamed data is included in the dashboard at the standard requested by the project stakeholders?

                        Answer: B

                        Explanation:
                        In this scenario, the data engineering team has configured a Structured Streaming pipeline that updates the gold-level tables every 10 minutes. To ensure that the dashboard reflects the most recent data, it is essential to set the dashboard ' s refresh schedule to an interval of 10 minutes or less. This synchronization ensures that stakeholders view the latest information shortly after it becomes available in the gold-level tables. Options B, C, and D do not directly address the requirement of aligning the dashboard refresh frequency with the data update interval.


                        NEW QUESTION # 85
                        ......

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