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Databricks-Certified-Data-Analyst-Associate Exam Preparation: Databricks Certified Data Analyst Associate Exam & Databricks-Certified-Data-Analyst-Associate Practice Labs

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

Topic
Details

Topic 1

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 2

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 3

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 4

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 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 (Q12-Q17):

NEW QUESTION # 12
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 granting the stakeholders' email addresses permissions to the dashboard
D. By adding the stakeholders' email addresses to the refresh schedule subscribers list

Answer: D

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.

NEW QUESTION # 13
Data professionals with varying responsibilities use the Databricks Lakehouse Platform Which role in the Databricks Lakehouse Platform use Databricks SQL as their primary service?

A. Data engineer
B. Data scientist
C. Business analyst
D. Platform architect

Answer: C

Explanation:
In the Databricks Lakehouse Platform, business analysts primarily utilize Databricks SQL as their main service. Databricks SQL provides an environment tailored for executing SQL queries, creating visualizations, and developing dashboards, which aligns with the typical responsibilities of business analysts who focus on interpreting data to inform business decisions. While data scientists and data engineers also interact with the Databricks platform, their primary tools and services differ; data scientists often engage with machine learning frameworks and notebooks, whereas data engineers focus on data pipelines and ETL processes. Platform architects are involved in designing and overseeing the infrastructure and architecture of the platform. Therefore, among the roles listed, business analysts are the primary users of Databricks SQL.

NEW QUESTION # 14
A data analyst is processing a complex aggregation on a table with zero null values and the query returns the following result:

Which query did the analyst execute in order to get this result?

Answer: D

NEW QUESTION # 15
How can a data analyst determine if query results were pulled from the cache?

A. Go to the Data tab and click Last Query. The details of the query will show if the results came from the cache.
B. Go to the Alerts tab and check the Cache Status alert.
C. Go to the Queries tab and click on Cache Status. The status will be green if the results from the last run came from the cache.
D. Go to the SQL Warehouse (formerly SQL Endpoints) tab and click on Cache. The Cache file will show the contents of the cache.
E. Go to the Query History tab and click on the text of the query. The slideout shows if the results came from the cache.

Answer: E

Explanation:
Databricks SQL uses a query cache to store the results of queries that have been executed previously. This improves the performance and efficiency of repeated queries. To determine if a query result was pulled from the cache, you can go to the Query History tab in the Databricks SQL UI and click on the text of the query. A slideout will appear on the right side of the screen, showing the query details, including the cache status. If the result came from the cache, the cache status will show "Cached". If the result did not come from the cache, the cache status will show "Not cached". You can also see the cache hit ratio, which is the percentage of queries that were served from the cache. Reference: The answer can be verified from Databricks SQL documentation which provides information on how to use the query cache and how to check the cache status. Reference link: Databricks SQL - Query Cache

NEW QUESTION # 16
A business analyst has been asked to create a data entity/object called sales_by_employee. It should always stay up-to-date when new data are added to the sales table. The new entity should have the columns sales_person, which will be the name of the employee from the employees table, and sales, which will be all sales for that particular sales person. Both the sales table and the employees table have an employee_id column that is used to identify the sales person.
Which of the following code blocks will accomplish this task?

Answer: C

Explanation:
The SQL code provided in Option D is the correct way to create a view named sales_by_employee that will always stay up-to-date with the sales and employees tables. The code uses the CREATE OR REPLACE VIEW statement to define a new view that joins the sales and employees tables on the employee_id column. It selects the employee_name as sales_person and all sales for each employee, ensuring that the data entity/object is always up-to-date when new data are added to these tables.

NEW QUESTION # 17
......

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