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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	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 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	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	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.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
A data analyst has been asked to produce a visualization that shows the flow of users through a website.
Which of the following is used for visualizing this type of flow?

A. Heatmap
B. IChoropleth
C. Pivot Table
D. Word Cloud
E. Sankey

Answer: E

Explanation:
A Sankey diagram is a type of visualization that shows the flow of data between different nodes or categories.
It is often used to represent the movement of users through a website, as it can show the paths they take, the sources they come from, the pages they visit, and the outcomes they achieve. A Sankey diagram consists of links and nodes, where the links represent the volume or weight of the flow, and the nodes represent the stages or steps of the flow. The width of the links is proportional to the amount of flow, and the color of the links can indicate different attributes or segments of the flow. A Sankey diagram can help identify the most common or popular user journeys, the bottlenecks or drop-offs in the flow, and the opportunities for improvement or optimization. References: The answer can be verified from Databricks documentation which provides examples and instructions on how to create Sankey diagrams using Databricks SQL Analytics and Databricks Visualizations. Reference links: Databricks SQL Analytics - Sankey Diagram, Databricks Visualizations - Sankey Diagram

NEW QUESTION # 18
A data analyst runs the following command:
SELECT age, country
FROM my_table
WHERE age >= 75 AND country = 'canada';
Which of the following tables represents the output of the above command?

Answer: C

Explanation:
The SQL query provided is designed to filter out records from "my_table" where the age is 75 or above and the country is Canada. Since I can't view the content of the links provided directly, I need to rely on the image attached to this question for context. Based on that, Option E (the image attached) represents a table with columns "age" and "country", showing records where age is 75 or above and country is Canada. Reference: The answer can be inferred from understanding SQL queries and their outputs as per Databricks documentation: Databricks SQL

NEW QUESTION # 19
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. Restore the table to the version from two weeks ago using the RESTORE command, and have the analyst query the restored table.
B. Use the VACUUM command to remove all versions of the table older than two weeks, then the analyst can query the remaining version.
C. 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.
D. Truncate the table to remove all data, then reload the data from two weeks ago into the truncated table for the analyst to query.

Answer: C

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 # 20
Which of the following is a benefit of Databricks SQL using ANSI SQL as its standard SQL dialect?

A. It allows for the use of Photon's computation optimizations
B. It is more compatible with Spark's interpreters
C. It has increased customization capabilities
D. It is easy to migrate existing SQL queries to Databricks SQL
E. It is more performant than other SQL dialects

Answer: D

Explanation:
Databricks SQL uses ANSI SQL as its standard SQL dialect, which means it follows the SQL specifications defined by the American National Standards Institute (ANSI). This makes it easier to migrate existing SQL queries from other data warehouses or platforms that also use ANSI SQL or a similar dialect, such as PostgreSQL, Oracle, or Teradata. By using ANSI SQL, Databricks SQL avoids surprises in behavior or unfamiliar syntax that may arise from using a non-standard SQL dialect, such as Spark SQL or Hive SQL12. Moreover, Databricks SQL also adds compatibility features to support common SQL constructs that are widely used in other data warehouses, such as QUALIFY, FILTER, and user-defined functions2. Reference: ANSI compliance in Databricks Runtime, Evolution of the SQL language at Databricks: ANSI standard by default and easier migrations from data warehouses

NEW QUESTION # 21
A data analyst has created a user-defined function using the following line of code:
CREATE FUNCTION price(spend DOUBLE, units DOUBLE)
RETURNS DOUBLE
RETURN spend / units;
Which of the following code blocks can be used to apply this function to the customer_spend and customer_units columns of the table customer_summary to create column customer_price?

A. SELECT price(customer_spend, customer_units) AS customer_price FROM customer_summary
B. SELECT double(price(customer_spend, customer_units)) AS customer_price FROM customer_summary
C. SELECT function(price(customer_spend, customer_units)) AS customer_price FROM customer_summary
D. SELECT PRICE customer_spend, customer_units AS customer_price FROM customer_summary
E. SELECT price FROM customer_summary

Answer: A

Explanation:
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

NEW QUESTION # 22
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