2025 High Hit-Rate Databricks Databricks-Certified-Data-Analyst-Associate Reliable Dumps Ebook

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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	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 contrast MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
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 (Q24-Q29):

NEW QUESTION # 24
The stakeholders.customers table has 15 columns and 3,000 rows of data. The following command is run:

After running SELECT * FROM stakeholders.eur_customers, 15 rows are returned. After the command executes completely, the user logs out of Databricks.
After logging back in two days later, what is the status of the stakeholders.eur_customers view?

A. The view remains available but attempting to SELECT from it results in an empty result set because data in views are automatically deleted after logging out.
B. The view remains available and SELECT * FROM stakeholders.eur_customers will execute correctly.
C. The view is not available in the metastore, but the underlying data can be accessed with SELECT * FROM delta. `stakeholders.eur_customers`.
D. The view has been dropped.
E. The view has been converted into a table.

Answer: D

Explanation:
The command you sent creates a TEMP VIEW, which is a type of view that is only visible and accessible to the session that created it. When the session ends or the user logs out, the TEMP VIEW is automatically dropped and cannot be queried anymore. Therefore, after logging back in two days later, the status of the stakeholders.eur_customers view is that it has been dropped and SELECT * FROM stakeholders.eur_customers will result in an error. The other options are not correct because:
A) The view does not remain available, as it is a TEMP VIEW that is dropped when the session ends or the user logs out.
C) The view is not available in the metastore, as it is a TEMP VIEW that is not registered in the metastore. The underlying data cannot be accessed with SELECT * FROM delta. stakeholders.eur_customers, as this is not a valid syntax for querying a Delta Lake table. The correct syntax would be SELECT * FROM delta.dbfs:/stakeholders/eur_customers, where the location path is enclosed in backticks. However, this would also result in an error, as the TEMP VIEW does not write any data to the file system and the location path does not exist.
D) The view does not remain available, as it is a TEMP VIEW that is dropped when the session ends or the user logs out. Data in views are not automatically deleted after logging out, as views do not store any data. They are only logical representations of queries on base tables or other views.
E) The view has not been converted into a table, as there is no automatic conversion between views and tables in Databricks. To create a table from a view, you need to use a CREATE TABLE AS statement or a similar command. Reference: CREATE VIEW | Databricks on AWS, Solved: How do temp views actually work? - Databricks - 20136, temp tables in Databricks - Databricks - 44012, Temporary View in Databricks - BIG DATA PROGRAMMERS, Solved: What is the difference between a Temporary View an ...

NEW QUESTION # 25
Which of the following is an advantage of using a Delta Lake-based data lakehouse over common data lake solutions?

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

Answer: B

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 # 26
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. Queries that use query parameters cannot be used with Alerts.
C. Alerts don't work with queries that access tables.
D. The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.
E. The wrong query parameter is being used. Alerts only work with Date and Time query parameters.

Answer: B

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 # 27
A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every 10 minutes.
A data analyst has created a dashboard based on this gold level dat
a. The project stakeholders want to see the results in the dashboard updated within 10 minutes or less of new data becoming available within the gold-level tables.
What is the ability to ensure the streamed data is included in the dashboard at the standard requested by the project stakeholders?

A. A refresh schedule with stakeholders included as subscribers
B. A refresh schedule with an interval of 10 minutes or less
C. A refresh schedule with a Structured Streaming cluster
D. A refresh schedule with an always-on SQL Warehouse (formerly known as SQL Endpoint

Answer: B

Explanation:
In this scenario, the data engineering team has configured a Structured Streaming pipeline that updates the gold-level tables every 10 minutes. To ensure that the dashboard reflects the most recent data, it is essential to set the dashboard's refresh schedule to an interval of 10 minutes or less. This synchronization ensures that stakeholders view the latest information shortly after it becomes available in the gold-level tables. Options B, C, and D do not directly address the requirement of aligning the dashboard refresh frequency with the data update interval.

NEW QUESTION # 28
A data analyst wants to create a dashboard with three main sections: Development, Testing, and Production. They want all three sections on the same dashboard, but they want to clearly designate the sections using text on the dashboard.
Which of the following tools can the data analyst use to designate the Development, Testing, and Production sections using text?

A. Separate endpoints for each section
B. Separate color palettes for each section
C. Direct text written into the dashboard in editing mode
D. Markdown-based text boxes
E. Separate queries for each section

Answer: D

Explanation:
Markdown-based text boxes are useful as labels on a dashboard. They allow the data analyst to add text to a dashboard using the %md magic command in a notebook cell and then select the dashboard icon in the cell actions menu. The text can be formatted using markdown syntax and can include headings, lists, links, images, and more. The text boxes can be resized and moved around on the dashboard using the float layout option. Reference: Dashboards in notebooks, How to add text to a dashboard in Databricks

NEW QUESTION # 29
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2025 HIGH HIT-RATE DATABRICKS DATABRICKS-CERTIFIED-DATA-ANALYST-ASSOCIATE RELIABLE DUMPS EBOOK

2025 High Hit-Rate Databricks Databricks-Certified-Data-Analyst-Associate Reliable Dumps Ebook