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

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

 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

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).

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.

The command INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers is not a valid syntax for inserting data into a table in Databricks SQL. According to the documentation12, the correct syntax for inserting data into a table is either:

INSERT { OVERWRITE | INTO } [ TABLE ] table_name [ PARTITION clause ] [ ( column_name [, ...] ) | BY NAME ] query

INSERT INTO [ TABLE ] table_name REPLACE WHERE predicate query

The command in the question is missing the OVERWRITE or INTO keyword, and the query part that specifies the source of the data to be inserted. The TABLE keyword is optional and can be omitted. The PARTITION clause and the column list are also optional and depend on the table schema and the data source. Therefore, the command in the question will fail with a syntax error.

To share a Databricks SQL dashboard with stakeholders who do not have accounts in the Databricks deployment and ensure they are notified upon each refresh, the data analyst can add the stakeholders' email addresses to the dashboard's refresh schedule subscribers list. This approach allows the stakeholders to receive email notifications containing the latest dashboard updates without requiring them to have direct access to the Databricks workspace. This method is efficient and minimizes effort, as it automates the notification process and ensures stakeholders remain informed of the most recent data insights.​

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'

AI-generated code. Review and use carefully. More info on FAQ.

Databricks SQL is a serverless data warehouse on the Lakehouse that lets you run all of your SQL and BI applications at scale with your tools of choice, all at a fraction of the cost of traditional cloud data warehouses1. Databricks SQL allows you to create SQL queries and data visualizations using the SQL Analytics UI or the Databricks SQL CLI2. You can also place your data visualizations within a dashboard and share it with other users in your organization3. Databricks SQL is powered by Delta Lake, which provides reliability, performance, and governance for your data lake4. References:

Databricks SQL

Query data using SQL Analytics

Visualizations in Databricks notebooks

Delta Lake

The Owner field in the table page shows the current owner of the table and allows the owner to change it to another user or group. To change the ownership of the table, the owner can click on the Owner field and select the new owner from the drop-down list. This will transfer the ownership of the table to the selected user or group and remove the previous owner from the list of table access control entries1. The other options are incorrect because:

A. Removing the owner’s account from the Owner field will not change the ownership of the table, but will make the table ownerless2.

B. Selecting All Users from the Owner field will not change the ownership of the table, but will grant all users access to the table3.

D. Selecting the Admins group from the Owner field will not change the ownership of the table, but will grant the Admins group access to the table3.

E. Removing all access from the Owner field will not change the ownership of the table, but will revoke all access to the table4. References:

1: Change table ownership

2: Ownerless tables

3: Table access control

4: Revoke access to a table

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.