Pass the Databricks Data Analyst Databricks-Certified-Data-Analyst-Associate Questions and answers with ValidTests

In Databricks, aviewis a saved SQL query definition that references existing tables or other views. Once created, a view remains persisted in themetastore(such as Unity Catalog or Hive Metastore) until it is explicitly dropped.

Key points:

Views donot store datathemselves but reference data from underlying tables.

Logging out or being inactive doesnotdelete or alter views.

Unless a user or admin explicitlydropsthe view or the underlying data/table is deleted, the view continues to function as expected.

Therefore, after logging back in—even days later—a user can still run SELECT * FROM stakeholders.eur_customers, and it will return the same data (provided the underlying table hasn’t changed).

 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

Delta Lake is a storage layer that enhances data lakes with features like ACID transactions, schema enforcement, and time travel. While it stores table data as Parquet files, Delta Lake also keeps a transaction log (stored in the _delta_log directory) that contains detailed table metadata.

This metadata includes:

Table schema

Partitioning information

Data file paths

Transactional operations like inserts, updates, and deletes

Commit history and version control

This metadata is critical for supporting Delta Lake’s advanced capabilities such as time travel and efficient query execution. Delta Lake doesnotstore data summary visualizations or owner account information directly alongside the data files.

 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

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.

The result set provided shows a combination of grouping by two columns (group_1andgroup_2) with subtotals for each level of grouping and a grand total. This pattern is typical of aGROUP BY ... WITH ROLLUPoperation in SQL, which provides subtotal rows and a grand total row in the result set.

Considering the query options:

A)Option A:GROUP BY group_1, group_2 INCLUDING NULL- This is not a standard SQL clause and would not result in subtotals and a grand total.

B)Option B:GROUP BY group_1, group_2 WITH ROLLUP- This would create subtotals for each uniquegroup_1, each combination ofgroup_1andgroup_2, and a grand total, which matches the result set provided.

C)Option C:GROUP BY group_1, group 2- This is a simpleGROUP BYand would not include subtotals or a grand total.

D)Option D:GROUP BY group_1, group_2, (group_1, group_2)- This syntax is not standard and would likely result in an error or be interpreted as a simpleGROUP BY, not providing the subtotals and grand total.

E)Option E:GROUP BY group_1, group_2 WITH CUBE- TheWITH CUBEoperation produces subtotals for all combinations of the selected columns and a grand total, which is more than what is shown in the result set.

The correct answer isOption B, which usesWITH ROLLUPto generate the subtotals for each level of grouping as well as a grand total. This matches the result set where we have subtotals for eachgroup_1, each combination ofgroup_1andgroup_2, and the grand total where bothgroup_1andgroup_2areNULL.

Descriptive statistics refer to statistical methods used to describe and summarize the basic features of data in a study. They provide simple summaries about the sample and the measures, often including metrics such as mean, median, mode, range, and standard deviation. Databricks learning materials highlight that descriptive statistics use summary statistics to quantitatively describe and summarize data, providing insight into data distributions without making inferences or predictions.

A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard. References: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL

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.​