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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 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 3

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 4

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 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.

Databricks Certified Data Analyst Associate Exam Sample Questions (Q58-Q63):

NEW QUESTION # 58
A data analyst created and is the owner of the managed table my_ table. They now want to change ownership of the table to a single other user using Data Explorer.
Which of the following approaches can the analyst use to complete the task?

A. Edit the Owner field in the table page by selecting the Admins group
B. Edit the Owner field in the table page by removing all access
C. Edit the Owner field in the table page by selecting All Users
D. Edit the Owner field in the table page by selecting the new owner's account
E. Edit the Owner field in the table page by removing their own account

Answer: D

Explanation:
The Owner field in the table page shows the current owner of the table and allows the owner to change it to another user or group. To change the ownership of the table, the owner can click on the Owner field and select the new owner from the drop-down list. This will transfer the ownership of the table to the selected user or group and remove the previous owner from the list of table access control entries1. The other options are incorrect because:
A) Removing the owner's account from the Owner field will not change the ownership of the table, but will make the table ownerless2.
B) Selecting All Users from the Owner field will not change the ownership of the table, but will grant all users access to the table3.
D) Selecting the Admins group from the Owner field will not change the ownership of the table, but will grant the Admins group access to the table3.
E) Removing all access from the Owner field will not change the ownership of the table, but will revoke all access to the table4. Reference:
1: Change table ownership
2: Ownerless tables
3: Table access control
4: Revoke access to a table

NEW QUESTION # 59
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. Sankey
E. Word Cloud

Answer: D

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. Reference: 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 # 60
Which of the following is an advantage of using a Delta Lake-based data lakehouse over common data lake solutions?

A. Scalable storage
B. Data deletion
C. Flexible schemas
D. Open-source formats
E. ACID transactions

Answer: E

Explanation:
A Delta Lake-based data lakehouse is a data platform architecture that combines the scalability and flexibility of a data lake with the reliability and performance of a data warehouse. One of the key advantages of using a Delta Lake-based data lakehouse over common data lake solutions is that it supports ACID transactions, which ensure data integrity and consistency. ACID transactions enable concurrent reads and writes, schema enforcement and evolution, data versioning and rollback, and data quality checks. These features are not available in traditional data lakes, which rely on file-based storage systems that do not support transactions. Reference:
Delta Lake: Lakehouse, warehouse, advantages | Definition
Synapse - Data Lake vs. Delta Lake vs. Data Lakehouse
Data Lake vs. Delta Lake - A Detailed Comparison
Building a Data Lakehouse with Delta Lake Architecture: A Comprehensive Guide

NEW QUESTION # 61
A data analyst has been asked to configure an alert for a query that returns the income in the accounts_receivable table for a date range. The date range is configurable using a Date query parameter.
The Alert does not work.
Which of the following describes why the Alert does not work?

A. Queries that return results based on dates cannot be used with Alerts.
B. The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.
C. Alerts don't work with queries that access tables.
D. Queries that use query parameters cannot be used with Alerts.
E. The wrong query parameter is being used. Alerts only work with Date and Time query parameters.

Answer: D

Explanation:
According to the Databricks documentation1, queries that use query parameters cannot be used with Alerts. This is because Alerts do not support user input or dynamic values. Alerts leverage queries with parameters using the default value specified in the SQL editor for each parameter. Therefore, if the query uses a Date query parameter, the alert will always use the same date range as the default value, regardless of the actual date. This may cause the alert to not work as expected, or to not trigger at all. Reference:
Databricks SQL alerts: This is the official documentation for Databricks SQL alerts, where you can find information about how to create, configure, and monitor alerts, as well as the limitations and best practices for using alerts.

NEW QUESTION # 62
Which of the following is a benefit of Databricks SQL using ANSI SQL as its standard SQL dialect?

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

Answer: A

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 # 63
......

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