GET DATABRICKS DATABRICKS-CERTIFIED-DATA-ANALYST-ASSOCIATE DUMPS FOR QUICK PREPARATION [2025]

Get Databricks Databricks-Certified-Data-Analyst-Associate Dumps For Quick Preparation [2025]

Get Databricks Databricks-Certified-Data-Analyst-Associate Dumps For Quick Preparation [2025]

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q28-Q33):

NEW QUESTION # 28
A data analyst is working with gold-layer tables to complete an ad-hoc project. A stakeholder has provided the analyst with an additional dataset that can be used to augment the gold-layer tables already in use.
Which of the following terms is used to describe this data augmentation?

  • A. Data testing
  • B. Ad-hoc improvements
  • C. Last-mile ETL
  • D. Last-mile
  • E. Data enhancement

Answer: E

Explanation:
Data enhancement is the process of adding or enriching data with additional information to improve its quality, accuracy, and usefulness. Data enhancement can be used to augment existing data sources with new data sources, such as external datasets, synthetic data, or machine learning models. Data enhancement can help data analysts to gain deeper insights, discover new patterns, and solve complex problems. Data enhancement is one of the applications of generative AI, which can leverage machine learning to generate synthetic data for better models or safer data sharing1.
In the context of the question, the data analyst is working with gold-layer tables, which are curated business-level tables that are typically organized in consumption-ready project-specific databases234. The gold-layer tables are the final layer of data transformations and data quality rules in the medallion lakehouse architecture, which is a data design pattern used to logically organize data in a lakehouse2. The stakeholder has provided the analyst with an additional dataset that can be used to augment the gold-layer tables already in use. This means that the analyst can use the additional dataset to enhance the existing gold-layer tables with more information, such as new features, attributes, or metrics. This data augmentation can help the analyst to complete the ad-hoc project more effectively and efficiently.
Reference:
What is the medallion lakehouse architecture? - Databricks
Data Warehousing Modeling Techniques and Their Implementation on the Databricks Lakehouse Platform | Databricks Blog What is the medallion lakehouse architecture? - Azure Databricks What is a Medallion Architecture? - Databricks Synthetic Data for Better Machine Learning | Databricks Blog


NEW QUESTION # 29
What describes Partner Connect in Databricks?

  • A. It exposes connection information to third-party tools via Databricks partners.
  • B. It is a feature that runs Databricks partner tools on a Databricks SQL Warehouse (formerly known as a SQL endpoint).
  • C. it allows for free use of Databricks partner tools through a common API.
  • D. it allows multi-directional connection between Databricks and Databricks partners easier.

Answer: D

Explanation:
Databricks Partner Connect is designed to simplify and streamline the integration between Databricks and its technology partners. It provides a unified interface within the Databricks platform that facilitates the discovery and connection to a variety of data, analytics, and AI tools. By automating the configuration of necessary resources such as clusters, tokens, and connection files, Partner Connect enables seamless, bi-directional data flow between Databricks and partner solutions. This integration enhances the overall functionality of the Databricks Lakehouse by allowing users to easily incorporate external tools and services into their workflows, thereby expanding the platform's capabilities and fostering a more cohesive data ecosystem.https://www.databricks.com/blog/2021/11/18/now-generally-available-introducing-databricks-partner-connect-to-discover-and-connect-popular-data-and-ai-tools-to-the-lakehouse?utm_source=chatgpt.com


NEW QUESTION # 30
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 using ORDER BY. which is not allowed in an aggregation.
  • B. The query is selecting region but region should only occur in the ORDER BY clause.
  • C. There are no mistakes in the query.
  • D. The query is missing a GROUP BY region clause.
  • E. The query is using count('). which will count all the customers in the customers table, no matter the region.

Answer: D

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 # 31
A data analyst creates a Databricks SQL Query where the result set has the following schema:
region STRING
number_of_customer INT
When the analyst clicks on the "Add visualization" button on the SQL Editor page, which of the following types of visualizations will be selected by default?

  • A. Line Chart
  • B. Violin Chart
  • C. Histogram
  • D. There is no default. The user must choose a visualization type.
  • E. IBar Chart

Answer: E

Explanation:
According to the Databricks SQL documentation, when a data analyst clicks on the "Add visualization" button on the SQL Editor page, the default visualization type is Bar Chart. This is because the result set has two columns: one of type STRING and one of type INT. The Bar Chart visualization automatically assigns the STRING column to the X-axis and the INT column to the Y-axis. The Bar Chart visualization is suitable for showing the distribution of a numeric variable across different categories. Reference: Visualization in Databricks SQL, Visualization types


NEW QUESTION # 32
What describes the variance of a set of values?

  • A. Variance is a measure of central tendency of a set of values.
  • B. Variance is a measure of how far a single observed value is from a set ot va IN
  • C. Variance is a measure of how far a set of values is spread out from the sets central value.
  • D. Variance is a measure of how far an observed value is from the variable's maximum or minimum value.

Answer: C

Explanation:
Variance is a statistical measure that quantifies the dispersion or spread of a set of values around their mean (central value). It is calculated by taking the average of the squared differences between each value and the mean of the dataset. A higher variance indicates that the data points are more spread out from the mean, while a lower variance suggests that they are closer to the mean. This measure is fundamental in statistics to understand the degree of variability within a dataset.WikipediaWikipedia+1Investopedia+1


NEW QUESTION # 33
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