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

Topic	Details
Topic 1	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 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	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 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	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 (Q23-Q28):

NEW QUESTION # 23
A data analyst needs to share a Databricks SQL dashboard with stakeholders that are not permitted to have accounts in the Databricks deployment. The stakeholders need to be notified every time the dashboard is refreshed.
Which approach can the data analyst use to accomplish this task with minimal effort/

A. By downloading the dashboard as a PDF and emailing it to the stakeholders each time it is refreshed
B. By granting the stakeholders ' email addresses to the SQL Warehouse (formerly known as endpoint) subscribers list
C. By adding the stakeholders ' email addresses to the refresh schedule subscribers list
D. By granting the stakeholders ' email addresses permissions to the dashboard

Answer: C

Explanation:
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.
Reference: Manage scheduled dashboard updates and subscriptions

NEW QUESTION # 24
A data organization has a team of engineers developing data pipelines following the medallion architecture using Delta Live Tables. While the data analysis team working on a project is using gold-layer tables from these pipelines, they need to perform some additional processing of these tables prior to performing their analysis.
Which of the following terms is used to describe this type of work?

A. Data testing
B. Data enhancement
C. Data blending
D. Last-mile
E. Last-mile ETL

Answer: E

Explanation:
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

NEW QUESTION # 25
In a healthcare provider organization using Delta Lake to store electronic health records, a data analyst needs to analyze a snapshot of the patient_records table from two weeks ago before some recent data corrections were applied.
What approach should the Data Engineer take to allow the analyst to query that specific prior version?

A. Truncate the table to remove all data, then reload the data from two weeks ago into the truncated table for the analyst to query.
B. Identify the version number corresponding to two weeks ago from the Delta transaction log, share that version number with the analyst to query using VERSION AS OF syntax, or export that version to a new Delta table for the analyst to query.
C. Use the VACUUM command to remove all versions of the table older than two weeks, then the analyst can query the remaining version.
D. Restore the table to the version from two weeks ago using the RESTORE command, and have the analyst query the restored table.

Answer: B

Explanation:
Option B is correct. Delta Lake supports time travel, which allows users to query previous table versions by timestamp or version number. The version can be obtained from table history and queried using VERSION AS OF. Restoring the table would change the current table state for everyone, which is not necessary when the analyst only needs to analyze a historical snapshot. VACUUM would remove old data files and can prevent time travel. Official Databricks extract: time travel supports "querying previous table versions," and the syntax includes SELECT * FROM people10m VERSION AS OF 123. Databricks also states that the version can be obtained from DESCRIBE HISTORY. Note: the two-week snapshot is queryable only if the required log and data files are still retained.

NEW QUESTION # 26
Data professionals with varying titles use the Databricks SQL service as the primary touchpoint with the Databricks Lakehouse Platform. However, some users will use other services like Databricks Machine Learning or Databricks Data Science and Engineering.
Which of the following roles uses Databricks SQL as a secondary service while primarily using one of the other services?

A. Data analyst
B. Business intelligence analyst
C. SQL analyst
D. Data engineer
E. Business analyst

Answer: D

Explanation:
Data engineers are primarily responsible for building, managing, and optimizing data pipelines and architectures. They use Databricks Data Science and Engineering service to perform tasks such as data ingestion, transformation, quality, and governance. Data engineers may use Databricks SQL as a secondary service to query, analyze, and visualize data from the lakehouse, but this is not their main focus. References: Databricks SQL overview, Databricks Data Science and Engineering overview, Data engineering with Databricks

NEW QUESTION # 27
A data analyst has been asked to use the below table sales_table to rank products within region by the sales.
Input table:
region product sales
WEST A 1880.59
EAST A 2045.99
EAST B 4583.23
WEST B 3391.19
The result of the query should look like this:
region product rank
EAST B 1
EAST A 2
WEST B 1
WEST A 2
Which query will accomplish this task?

A. Option D
B. SELECT region, product, RANK() OVER ( PARTITION BY region ) AS rankFROM sales_table;
C. SELECT region, product, RANK() OVER ( ORDER BY sales DESC ) AS rankFROM sales_table;
D. SELECT region, product, RANK() OVER ( PARTITION BY product ORDER BY sales DESC ) AS rankFROM sales_table;
E. Option B
F. SELECT region, product, RANK() OVER ( PARTITION BY region ORDER BY sales DESC ) AS rankFROM sales_table;
G. Option A
H. Option C

Answer: F

Explanation:
Option D is correct. The phrase "rank products within region by the sales" means each region must be ranked separately, so the window must use PARTITION BY region. The ranking must be based on highest sales first, so the window must use ORDER BY sales DESC. Option A ranks across all regions together. Option B ranks within product, not within region. Option C does not specify the sales ordering. Official Databricks extract:
rank() "returns the rank of a value compared to all values in the partition," and its OVER clause must include ORDER BY.

NEW QUESTION # 28
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