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

Topic	Details
Topic 1	Analytics applications: It describes key moments of statistical distributions, data enhancement, and the blending of data between two source applications. Moroever, the topic also explains last-mile ETL, a scenario in which data blending would be beneficial, key statistical measures, descriptive statistics, and discrete and continuous statistics.
Topic 2	Data Management: The topic describes Delta Lake as a tool for managing data files, Delta Lake manages table metadata, benefits of Delta Lake within the Lakehouse, tables on Databricks, a table owner’s responsibilities, and the persistence of data. It also identifies management of a table, usage of Data Explorer by a table owner, and organization-specific considerations of PII data. Lastly, the topic it explains how the LOCATION keyword changes, usage of Data Explorer to secure data.
Topic 3	SQL in the Lakehouse: It identifies a query that retrieves data from the database, the output of a SELECT query, a benefit of having ANSI SQL, access, and clean silver-level data. It also compares and contrasts MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
Topic 4	Data Visualization and Dashboarding: Sub-topics of this topic are about of describing how notifications are sent, how to configure and troubleshoot a basic alert, how to configure a refresh schedule, the pros and cons of sharing dashboards, how query parameters change the output, and how to change the colors of all of the visualizations. It also discusses customized data visualizations, visualization formatting, Query Based Dropdown List, and the method for sharing a dashboard.
Topic 5	Databricks SQL: This topic discusses key and side audiences, users, Databricks SQL benefits, complementing a basic Databricks SQL query, schema browser, Databricks SQL dashboards, and the purpose of Databricks SQL endpoints warehouses. Furthermore, the delves into Serverless Databricks SQL endpoint warehouses, trade-off between cluster size and cost for Databricks SQL endpoints warehouses, and Partner Connect. Lastly it discusses small-file upload, connecting Databricks SQL to visualization tools, the medallion architecture, the gold layer, and the benefits of working with streaming data.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q109-Q114):

NEW QUESTION # 109
A data analyst filters rows where the tags array includes the value ' sql ' using this query:
SELECT *
FROM main.analytics.articles
WHERE tags = ' sql ' ;
This query returns no results.
How should the analyst query to filter for rows where the tags array contains ' sql ' ?

A. SELECT * FROM main.analytics.articles WHERE ' sql ' IN tags;
B. SELECT * FROM main.analytics.articles WHERE tags LIKE ' %sql% ' ;
C. SELECT * FROM main.analytics.articles WHERE tags IN ( ' sql ' );
D. SELECT * FROM main.analytics.articles WHERE array_contains(tags, ' sql ' );

Answer: D

Explanation:
Option D is correct. The column tags is an array, so comparing the entire array to the scalar string ' sql ' is incorrect. The correct Databricks SQL function is array_contains(array, value), which returns true when the array contains the specified value. The uploaded document has a spelling error, array_conatins; the corrected Databricks function name is array_contains. Official Databricks documentation states that array_contains
"returns true if array contains value."

NEW QUESTION # 110
Delta Lake stores table data as a series of data files, but it also stores a lot of other information.
Which of the following is stored alongside data files when using Delta Lake?

A. Table metadata
B. Owner account information
C. Data summary visualizations
D. Table metadata, data summary visualizations, and owner account information
E. None of these

Answer: A

Explanation:
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 does not store data summary visualizations or owner account information directly alongside the data files.
Reference: Delta Lake Table Features - Databricks Documentation

NEW QUESTION # 111
A data analyst runs the following command:
INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers;
What is the result of running this command?

A. The suppliers table now contains only the data from the new suppliers table.
B. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, and any duplicate data is deleted.
C. The command fails because it is written incorrectly.
D. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, including any duplicate data.
E. The suppliers table now contains the data from the new suppliers table, and the new suppliers table now contains the data from the suppliers table.

Answer: C

Explanation:
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.
INSERT | Databricks on AWS
INSERT - Azure Databricks - Databricks SQL | Microsoft Learn

NEW QUESTION # 112
Which of the following layers of the medallion architecture is most commonly used by data analysts?

A. Silver
B. Bronze
C. All of these layers are used equally by data analysts
D. None of these layers are used by data analysts
E. Gold

Answer: E

Explanation:
The gold layer of the medallion architecture contains data that is highly refined and aggregated, and powers analytics, machine learning, and production applications. Data analysts typically use the gold layer to access data that has been transformed into knowledge, rather than just information. The gold layer represents the final stage of data quality and optimization in the lakehouse. References: What is the medallion lakehouse architecture?

NEW QUESTION # 113
Data engineers and data analysts are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?

A. The pipeline will need to be written entirely in SQL.
B. The pipeline will need to be written entirely in Python.
C. The pipeline will need to use a batch source in place of a streaming source.
D. The pipeline can have different notebook sources in SQL and Python.

Answer: D

Explanation:
Option A is correct. Delta Live Tables, now documented under Lakeflow Declarative Pipelines, supports both Python and SQL source files in the same pipeline. The pipeline does not need to be rewritten entirely in one language, and it can still use streaming inputs. Official Databricks extract: "You can add Python and SQL source files to the same pipeline." The same documentation also shows examples for batch and streaming reads in Python and SQL.

NEW QUESTION # 114
......

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