Databricks Certified Data Analyst Associate Exam Valid Test Topics & Databricks-Certified-Data-Analyst-Associate Free Download Demo & Databricks Certified Data Analyst Associate Exam Practice Test Training

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

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 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.
Topic 5	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.

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Actual Databricks Databricks-Certified-Data-Analyst-Associate Exam Questions in PDF

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q51-Q56):

NEW QUESTION # 51
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 missing a GROUP BY region clause.
B. The query is using ORDER BY. which is not allowed in an aggregation.
C. There are no mistakes in the query.
D. The query is using count('). which will count all the customers in the customers table, no matter the region.
E. The query is selecting region but region should only occur in the ORDER BY clause.

Answer: A

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 # 52
In which of the following situations should a data analyst use higher-order functions?

A. When custom logic needs to be converted to Python-native code
B. When built-in functions are taking too long to perform tasks
C. When custom logic needs to be applied at scale to array data objects
D. When built-in functions need to run through the Catalyst Optimizer
E. When custom logic needs to be applied to simple, unnested data

Answer: C

Explanation:
Higher-order functions are a simple extension to SQL to manipulate nested data such as arrays. A higher-order function takes an array, implements how the array is processed, and what the result of the computation will be. It delegates to a lambda function how to process each item in the array. This allows you to define functions that manipulate arrays in SQL, without having to unpack and repack them, use UDFs, or rely on limited built-in functions. Higher-order functions provide a performance benefit over user defined functions. Reference: Higher-order functions | Databricks on AWS, Working with Nested Data Using Higher Order Functions in SQL on Databricks | Databricks Blog, Higher-order functions - Azure Databricks | Microsoft Learn, Optimization recommendations on Databricks | Databricks on AWS

NEW QUESTION # 53
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: C

NEW QUESTION # 54
Which of the following statements describes descriptive statistics?

A. A branch of statistics that uses quantitative variables that must take on a finite or countably infinite set of values.
B. A branch of statistics that uses summary statistics to categorically describe and summarize data.
C. A branch of statistics that uses summary statistics to quantitatively describe and summarize data.
D. A branch of statistics that uses quantitative variables that must take on an uncountable set of values.
E. A branch of statistics that uses a variety of data analysis techniques to infer properties of an underlying distribution of probability.

Answer: C

Explanation:
Descriptive statistics is a branch of statistics that uses summary statistics, such as mean, median, mode, standard deviation, range, frequency, or correlation, to quantitatively describe and summarize data. Descriptive statistics can help data analysts understand the main features of a data set, such as its central tendency, variability, or distribution. Descriptive statistics can also help data analysts visualize data using charts, graphs, or tables. Descriptive statistics do not make any inferences or predictions about the data, unlike inferential statistics, which use data analysis techniques to infer properties of an underlying population or probability distribution from a sample of data. Reference: Databricks - Descriptive Statistics, Databricks - Data Analysis with Databricks SQL

NEW QUESTION # 55
A data analyst wants to create a Databricks SQL dashboard with multiple data visualizations and multiple counters. What must be completed before adding the data visualizations and counters to the dashboard?

A. The dashboard owner must also be the owner of the queries, data visualizations, and counters.
B. A SQL warehouse (formerly known as SQL endpoint) must be turned on and selected.
C. A markdown-based tile must be added to the top of the dashboard displaying the dashboard's name.
D. All data visualizations and counters must be created using Queries.

Answer: D

Explanation:
In Databricks SQL, when creating a dashboard that includes multiple data visualizations and counters, it is imperative that each visualization and counter is based on a query. The process involves the following steps:
Develop Queries:
For each desired visualization or counter, write a SQL query that retrieves the necessary data.
Create Visualizations and Counters:
After executing each query, utilize the results to create corresponding visualizations or counters. Databricks SQL offers a variety of visualization types to represent data effectively.
Assemble the Dashboard:
Add the created visualizations and counters to your dashboard, arranging them as needed to convey the desired insights.
By ensuring that all components of the dashboard are derived from queries, you maintain consistency, accuracy, and the ability to refresh data as needed. This approach also facilitates easier maintenance and updates to the dashboard elements.

NEW QUESTION # 56
......

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DATABRICKS CERTIFIED DATA ANALYST ASSOCIATE EXAM VALID TEST TOPICS & DATABRICKS-CERTIFIED-DATA-ANALYST-ASSOCIATE FREE DOWNLOAD DEMO & DATABRICKS CERTIFIED DATA ANALYST ASSOCIATE EXAM PRACTICE TEST TRAINING

Databricks Certified Data Analyst Associate Exam Valid Test Topics & Databricks-Certified-Data-Analyst-Associate Free Download Demo & Databricks Certified Data Analyst Associate Exam Practice Test Training