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[General] New Databricks-Certified-Data-Analyst-Associate Exam Testking, Databricks-Certif

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【General】 New Databricks-Certified-Data-Analyst-Associate Exam Testking, Databricks-Certif

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Databricks Certified Data Analyst Associate Exam exam practice questions play a crucial role in Databricks Certified Data Analyst Associate Exam Databricks-Certified-Data-Analyst-Associate exam preparation and give you insights Databricks Certified Data Analyst Associate Exam exam view. You are aware of the Databricks Certified Data Analyst Associate Exam Databricks-Certified-Data-Analyst-Associate exam topics, structure, and a number of the questions that you will face in the upcoming Databricks Certified Data Analyst Associate Exam Databricks-Certified-Data-Analyst-Associate Exam. You can evaluate your Salesforce Databricks Certified Data Analyst Associate Exam exam preparation performance and work on the weak topic areas. But here is the problem where you will get Databricks Certified Data Analyst Associate Exam exam questions.
Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:
TopicDetails
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
  • 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
  • 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 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 (Q40-Q45):NEW QUESTION # 40
A data scientist has asked a data analyst to create histograms for every continuous variable in a data set. The data analyst needs to identify which columns are continuous in the data set.
What describes a continuous variable?
  • A. A quantitative variable that can take on an uncountable set of values
  • B. A quantitative variable Chat can take on a finite or countably infinite set of values
  • C. A quantitative variable that never stops changing
  • D. A categorical variable in which the number of categories continues to increase over time
Answer: A
Explanation:
A continuous variable is a type of quantitative variable that can assume an infinite number of values within a given range. This means that between any two possible values, there can be an infinite number of other values. For example, variables such as height, weight, and temperature are continuous because they can be measured to any level of precision, and there are no gaps between possible values. This is in contrast to discrete variables, which can only take on specific, distinct values (e.g., the number of children in a family). Understanding the nature of continuous variables is crucial for data analysts, especially when selecting appropriate statistical methods and visualizations, such as histograms, to accurately represent and analyze the data.

NEW QUESTION # 41
Which of the following benefits of using Databricks SQL is provided by Data Explorer?
  • A. It can be used to view metadata and data, as well as view/change permissions.
  • B. It can be used to make visualizations that can be shared with stakeholders.
  • C. It can be used to run UPDATE queries to update any tables in a database.
  • D. It can be used to produce dashboards that allow data exploration.
  • E. It can be used to connect to third party Bl cools.
Answer: A
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 # 42
Which of the following describes how Databricks SQL should be used in relation to other business intelligence (BI) tools like Tableau, Power BI, and looker?
  • A. As a complementary tool for professional-grade presentations
  • B. As an exact substitute with the same level of functionality
  • C. As a complete replacement with additional functionality
  • D. As a substitute with less functionality
  • E. As a complementary tool for quick in-platform Bl work
Answer: E
Explanation:
Databricks SQL is not meant to replace or substitute other BI tools, but rather to complement them by providing a fast and easy way to query, explore, and visualize data on the lakehouse using the built-in SQL editor, visualizations, and dashboards. Databricks SQL also integrates seamlessly with popular BI tools like Tableau, Power BI, and Looker, allowing analysts to use their preferred tools to access data through Databricks clusters and SQL warehouses. Databricks SQL offers low-code and no-code experiences, as well as optimized connectors and serverless compute, to enhance the productivity and performance of BI workloads on the lakehouse. Reference: Databricks SQL, Connecting Applications and BI Tools to Databricks SQL, Databricks integrations overview, Databricks SQL: Delivering a Production SQL Development Experience on the Lakehouse

NEW QUESTION # 43
A data engineer is working with a nested array column products in table transactions. They want to expand the table so each unique item in products for each row has its own row where the transaction_id column is duplicated as necessary.
They are using the following incomplete command:

Which of the following lines of code can they use to fill in the blank in the above code block so that it successfully completes the task?
  • A. flatten(produces)
  • B. array distinct(produces)
  • C. reduce(produces)
  • D. explode(produces)
  • E. array(produces)
Answer: D
Explanation:
The explode function is used to transform a DataFrame column of arrays or maps into multiple rows, duplicating the other column's values. In this context, it will be used to expand the nested array column products in the transactions table so that each unique item in products for each row has its own row and the transaction_id column is duplicated as necessary. Reference: Databricks Documentation I also noticed that you sent me an image along with your message. The image shows a snippet of SQL code that is incomplete. It begins with "SELECT" indicating a query to retrieve data. "transaction_id," suggests that transaction_id is one of the columns being selected. There are blanks indicated by underscores where certain parts of the SQL command should be, including what appears to be an alias for a column and part of the FROM clause. The query ends with "FROM transactions;" indicating data is being selected from a 'transactions' table.
If you are interested in learning more about Databricks Data Analyst Associate certification, you can check out the following resources:
Databricks Certified Data Analyst Associate: This is the official page for the certification exam, where you can find the exam guide, registration details, and preparation tips.
Data Analysis With Databricks SQL: This is a self-paced course that covers the topics and skills required for the certification exam. You can access it for free on Databricks Academy.
Tips for the Databricks Certified Data Analyst Associate Certification: This is a blog post that provides some useful advice and study tips for passing the certification exam.
Databricks Certified Data Analyst Associate Certification: This is another blog post that gives an overview of the certification exam and its benefits.

NEW QUESTION # 44
A data analyst has been asked to count the number of customers in each region and has written the following query:

If there is a mistake in the query, which of the following describes the mistake?
  • A. The query is selecting region but region should only occur in the ORDER BY clause.
  • B. The query is using count('). which will count all the customers in the customers table, no matter the region.
  • C. The query is missing a GROUP BY region clause.
  • D. There are no mistakes in the query.
  • E. The query is using ORDER BY. which is not allowed in an aggregation.
Answer: C
Explanation:
In the provided SQL query, the data analyst is trying to count the number of customers in each region. However, they made a mistake by not including the "GROUP BY" clause to group the results by region. Without this clause, the query will not return counts for each distinct region but rather an error or incorrect result. Reference: The need for a GROUP BY clause in such queries can be understood from Databricks SQL documentation: Databricks SQL.
I also noticed that you uploaded an image with your question. The image shows a snippet of an SQL query written in plain text on a white background. The query is attempting to select regions and count customers from a "customers" table and order the results by region. There's no visible syntax highlighting or any other color - it's monochromatic. The query is the same as the one in your question. I'm not sure why you included the image, but maybe you wanted to show me the exact format of your query. If so, you can also use code blocks to display formatted content such as SQL queries. For example, you can write:
SELECT region, count(*) AS number_of_customers
FROM customers
ORDER BY region;
This way, you can avoid uploading images and make your questions more clear and concise. I hope this helps.

NEW QUESTION # 45
......
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