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| 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 (Q29-Q34):
NEW QUESTION # 29
What describes the variance of a set of values?
- A. Variance is a measure of how far an observed value is from the variable ' s maximum or minimum value.
- B. Variance is a measure of how far a single observed value is from a set ot va IN
- C. Variance is a measure of central tendency of a set of values.
- D. Variance is a measure of how far a set of values is spread out from the sets central value.
Answer: D
Explanation:
Variance is a statistical measure that quantifies the dispersion or spread of a set of values around their mean (central value). It is calculated by taking the average of the squared differences between each value and the mean of the dataset. A higher variance indicates that the data points are more spread out from the mean, while a lower variance suggests that they are closer to the mean. This measure is fundamental in statistics to understand the degree of variability within a dataset.WikipediaWikipedia+1Investopedia+1 Reference: Variance - Wikipedia
NEW QUESTION # 30
Which of the following benefits of using Databricks SQL is provided by Data Explorer?
- A. It can be used to make visualizations that can be shared with stakeholders.
- B. It can be used to produce dashboards that allow data exploration.
- C. It can be used to run UPDATE queries to update any tables in a database.
- D. It can be used to connect to third party Bl cools.
- E. It can be used to view metadata and data, as well as view/change permissions.
Answer: E
Explanation:
Data Explorer is a user interface that allows you to discover and manage data, schemas, tables, models, and permissions in Databricks SQL. You can use Data Explorer to view schema details, preview sample data, and see table and model details and properties. Administrators can view and change owners, and admins and data object owners can grant and revoke permissions1. Reference: Discover and manage data using Data Explorer
NEW QUESTION # 31
A data team has been given a series of projects by a consultant that need to be implemented in the Databricks Lakehouse Platform.
Which of the following projects should be completed in Databricks SQL?
- A. Combining two data sources into a single, comprehensive dataset
- B. Testing the quality of data as it is imported from a source
- C. Automating complex notebook-based workflows with multiple tasks
- D. Segmenting customers into like groups using a clustering algorithm
- E. Tracking usage of feature variables for machine learning projects
Answer: A
Explanation:
Databricks SQL is a service that allows users to query data in the lakehouse using SQL and create visualizations and dashboards1. One of the common use cases for Databricks SQL is to combine data from different sources and formats into a single, comprehensive dataset that can be used for further analysis or reporting2. For example, a data analyst can use Databricks SQL to join data from a CSV file and a Parquet file, or from a Delta table and a JDBC table, and create a new table or view that contains the combined data3. This can help simplify the data management and governance, as well as improve the data quality and consistency. Reference:
Databricks SQL overview
Databricks SQL use cases
Joining data sources
NEW QUESTION # 32
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. Dashboard Editor # Axes # Y Axis
- C. Settings # User Settings # Scaling
- 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 # 33
Which statement about visualizations is true?
- A. There is no difference between the bar chart and a histogram in Databricks SQL.
- B. Line charts are the preferred visualization type for categorical data.
- C. Different visualizations can be used to tell different stories about the data.
- D. All visualizations must use the same data in order to be included in the same Databricks SQL dashboard.
Answer: C
Explanation:
Option C is correct. Different visualization types are used to communicate different insights. For example, a bar chart may compare categories, a line chart may show change over time, and a table may show detailed records. Databricks dashboard documentation confirms that dashboard visualizations can select datasets, choose visualization types, and define x-axis and y-axis fields. The official exam guide also includes identifying the effective visualization type to communicate insights clearly. Option A is false because dashboard visualizations can be configured independently. Option B is false because categorical data is commonly shown with bar charts, not line charts. Option D is false because bar charts and histograms serve different analytical purposes. References: Databricks dashboard visualization documentation and Data Analyst Associate Exam Guide.
NEW QUESTION # 34
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