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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	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 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 (Q34-Q39):

NEW QUESTION # 34
In which of the following situations will the mean value and median value of variable be meaningfully different?

A. When the variable contains no outliers
B. When the variable contains a lot of extreme outliers
C. When the variable is of the categorical type
D. When the variable is of the boolean type
E. When the variable contains no missing values

Answer: B

Explanation:
The mean value of a variable is the average of all the values in a data set, calculated by dividing the sum of the values by the number of values. The median value of a variable is the middle value of the ordered data set, or the average of the middle two values if the data set has an even number of values. The mean value is sensitive to outliers, which are values that are very different from the rest of the data. Outliers can skew the mean value and make it less representative of the central tendency of the data. The median value is more robust to outliers, as it only depends on the middle values of the data. Therefore, when the variable contains a lot of extreme outliers, the mean value and the median value will be meaningfully different, as the mean value will be pulled towards the outliers, while the median value will remain close to the majority of the data1. References: Difference Between Mean and Median in Statistics (With Example) - BYJU'S

NEW QUESTION # 35
A data analyst needs to create an empty managed table table_name in database database_name with a specific schema. The table needs to be recreated and empty, regardless of whether or not the table already exists.
Which command can the analyst use to complete the task?

A. CREATE TABLE database_name.table_name USING (width INT, length INT, height INT);
B. CREATE OR REPLACE TABLE table_name FROM database_name USING (width INT, length INT, height INT);
C. CREATE OR REPLACE TABLE database_name.table_name (width INT, length INT, height INT);
D. CREATE OR REPLACE TABLE database_name.table_name USING (width INT, length INT, height INT);

Answer: C

Explanation:
The correct answer is C because the task requires creating or replacing a managed table with an explicitly defined schema. In Databricks SQL, column definitions are placed directly after the table name inside parentheses. The USING clause is for specifying a data source format, not for defining columns. Option C correctly uses CREATE OR REPLACE TABLE database_name.table_name (...), which recreates the table if it already exists and creates an empty table with the specified schema.
Official documentation extract used: Databricks CREATE TABLE syntax supports [CREATE OR] REPLACE followed by the table name and a table_specification containing column identifiers and column types.

NEW QUESTION # 36
Which statement describes descriptive statistics?

A. A branch of statistics that uses summary statistics to categorically describe and summarize data.
B. A branch of statistics that uses summary statistics to quantitatively describe and summarize data.
C. A branch of statistics that uses a variety of data analysis techniques to infer properties of an underlying distribution of probability.
D. A branch of statistics that uses quantitative variables that must take on a finite or countably infinite set of values.

Answer: B

Explanation:
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.

NEW QUESTION # 37
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. Last-mile ETL
B. Last-mile
C. Data blending
D. Data testing
E. Data enhancement

Answer: A

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 # 38
A data analyst has produced a visualization. A stakeholder has viewed the visualization and is complaining that the visualization is difficult to interpret. After looking at the visualization, the analyst determines that the scale of the y-axis must be changed.
Where are the controls for changing the scale of the y-axis in Databricks SQL?

A. Visualization Editor # Y Axis
B. Settings # User Settings # Scaling
C. Dashboard Editor # Axes # Y Axis
D. Query Editor # Y Axis

Answer: A

Explanation:
The correct answer is C because axis settings are configured in the visualization editor, specifically under the Y-axis settings. The Query Editor is for writing SQL, not configuring chart axes. User Settings do not control individual chart scales. Dashboard editing may contain visualizations, but the specific control is in the visualization editor's Y Axis configuration.
Official documentation extract used: Databricks chart options state that to configure formatting for the X axis or Y axis, click the corresponding axis tab, and the Y-axis supports scale and range controls.

NEW QUESTION # 39
......

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