External tables are tables that are defined in the Databricks metastore using the information stored in a cloud object storage location. External tables do not manage the data, but provide a schema and a table name to query the data. To create an external table, you can use the CREATE EXTERNAL TABLE statement and specify the object storage path to the LOCATION clause. For example, to create an external table named ext_table on a Parquet file stored in S3, you can use the following statement:
SQL
CREATE EXTERNAL TABLE ext_table (
col1 INT,
col2 STRING
)
STORED AS PARQUET
LOCATION 's3://bucket/path/file.parquet'
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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

In Databricks SQL, a subquery is a nested query within a larger SQL query that allows for the retrieval of data without the necessity of creating a table or view. This is particularly useful for simplifying complex queries by breaking them down into more manageable parts. Subqueries can be employed in various clauses such as SELECT, FROM, and WHERE to perform operations like filtering, transforming, and aggregating data on-the-fly. This flexibility enhances query efficiency and readability without the overhead of persisting intermediate results as separate tables or views.

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. Reference: Visualization in Databricks SQL, Visualization types

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.