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

Topic	Details
Topic 1	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.
Topic 2	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 3	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 4	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 5	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.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q103-Q108):

NEW QUESTION # 103
The stakeholders.customers table has 15 columns and 3,000 rows of data. The following command is run:

After running SELECT * FROM stakeholders.eur_customers, 15 rows are returned. After the command executes completely, the user logs out of Databricks.
After logging back in two days later, what is the status of the stakeholders.eur_customers view?

A. The view is not available in the metastore, but the underlying data can be accessed with SELECT * FROM delta. `stakeholders.eur_customers`.
B. The view remains available but attempting to SELECT from it results in an empty result set because data in views are automatically deleted after logging out.
C. The view has been dropped.
D. The view remains available and SELECT * FROM stakeholders.eur_customers will execute correctly.
E. The view has been converted into a table.

Answer: C

Explanation:
The command you sent creates a TEMP VIEW, which is a type of view that is only visible and accessible to the session that created it. When the session ends or the user logs out, the TEMP VIEW is automatically dropped and cannot be queried anymore. Therefore, after logging back in two days later, the status of the stakeholders.eur_customers view is that it has been dropped and SELECT * FROM stakeholders.eur_customers will result in an error. The other options are not correct because:
A) The view does not remain available, as it is a TEMP VIEW that is dropped when the session ends or the user logs out.
C) The view is not available in the metastore, as it is a TEMP VIEW that is not registered in the metastore. The underlying data cannot be accessed with SELECT * FROM delta. stakeholders.eur_customers, as this is not a valid syntax for querying a Delta Lake table. The correct syntax would be SELECT * FROM delta.dbfs:/stakeholders/eur_customers, where the location path is enclosed in backticks. However, this would also result in an error, as the TEMP VIEW does not write any data to the file system and the location path does not exist.
D) The view does not remain available, as it is a TEMP VIEW that is dropped when the session ends or the user logs out. Data in views are not automatically deleted after logging out, as views do not store any data. They are only logical representations of queries on base tables or other views.
E) The view has not been converted into a table, as there is no automatic conversion between views and tables in Databricks. To create a table from a view, you need to use a CREATE TABLE AS statement or a similar command. Reference: CREATE VIEW | Databricks on AWS, Solved: How do temp views actually work? - Databricks - 20136, temp tables in Databricks - Databricks - 44012, Temporary View in Databricks - BIG DATA PROGRAMMERS, Solved: What is the difference between a Temporary View an ...

NEW QUESTION # 104
A data analyst has come across a column in a table that contains personally identifiable information (PII). The data analyst should not have access to this type of PII data.
How should the data analyst proceed?

A. Stop working with the data and notify their supervisor to ensure the data is handled following organizational and legal best practices.
B. Stop working with the data and delete the table and any existing metadata or data files.
C. Stop working with the data and proceed with the project using other data.
D. Drop the column containing PII data and continue with the project without notifying anybody.

Answer: A

Explanation:
Option C is correct. If an analyst encounters PII that they should not access, they should stop using that data and escalate through the appropriate organizational process. They should not delete the table, drop the column, or silently continue. Databricks security and privacy guidance emphasizes least-privilege access, data classification by sensitivity, access control, and meeting compliance/privacy requirements, including redaction or obfuscation of PII when required. Therefore, notifying a supervisor or data owner is the correct professional and governance-aligned response. References: Databricks security, compliance, and privacy documentation.

NEW QUESTION # 105
A data analyst wants to create a Databricks SQL dashboard with multiple data visualizations and multiple counters. What must be completed before adding the data visualizations and counters to the dashboard?

A. All data visualizations and counters must be created using Queries.
B. A SQL warehouse (formerly known as SQL endpoint) must be turned on and selected.
C. The dashboard owner must also be the owner of the queries, data visualizations, and counters.
D. A markdown-based tile must be added to the top of the dashboard displaying the dashboard ' s name.

Answer: A

Explanation:
In Databricks SQL, when creating a dashboard that includes multiple data visualizations and counters, it is imperative that each visualization and counter is based on a query. The process involves the following steps:
* Develop Queries:
* For each desired visualization or counter, write a SQL query that retrieves the necessary data.
* Create Visualizations and Counters:
* After executing each query, utilize the results to create corresponding visualizations or counters.
Databricks SQL offers a variety of visualization types to represent data effectively.
* Assemble the Dashboard:
* Add the created visualizations and counters to your dashboard, arranging them as needed to convey the desired insights.
By ensuring that all components of the dashboard are derived from queries, you maintain consistency, accuracy, and the ability to refresh data as needed. This approach also facilitates easier maintenance and updates to the dashboard elements.
Reference: Visualization in Databricks SQL

NEW QUESTION # 106
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, data summary visualizations, and owner account information
B. Table metadata
C. Data summary visualizations
D. None of these
E. Owner account information

Answer: B

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 # 107
Which of the following SQL keywords can be used to convert a table from a long format to a wide format?

A. WHERE
B. PIVOT
C. SUM
D. CONVERT
E. TRANSFORM

Answer: B

Explanation:
Option B is correct. PIVOT rotates unique values from rows into separate columns, which is exactly the process of converting long-format data into wide-format data. UNPIVOT does the reverse. SUM is an aggregate function, WHERE filters rows, and TRANSFORM is not the SQL keyword used for this reshaping task. Official Databricks extract: the PIVOT clause "rotat[es] unique values from a column into separate columns."

NEW QUESTION # 108
......

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