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

The questions for Databricks-Certified-Data-Analyst-Associate were last updated on Oct 2, 2024.
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Question No. 1

Which of the following statements about adding visual appeal to visualizations in the Visualization Editor is incorrect?

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Correct Answer: D

The Visualization Editor in Databricks SQL allows users to create and customize various types of charts and visualizations from the query results. Users can change the visualization type, select the data fields, adjust the colors, format the data labels, and modify the tooltips. However, there is no option to add borders to the visualizations in the Visualization Editor.Borders are not a supported feature of the new chart visualizations in Databricks1. Therefore, the statement that borders can be added is incorrect.Reference:

New chart visualizations in Databricks | Databricks on AWS


Question No. 2

A data team has been given a series of projects by a consultant that need to be implemented in the Databricks Lakehouse Platform.

Which of the following projects should be completed in Databricks SQL?

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Correct Answer: C

Databricks SQL is a service that allows users to query data in the lakehouse using SQL and create visualizations and dashboards1.One of the common use cases for Databricks SQL is to combine data from different sources and formats into a single, comprehensive dataset that can be used for further analysis or reporting2.For example, a data analyst can use Databricks SQL to join data from a CSV file and a Parquet file, or from a Delta table and a JDBC table, and create a new table or view that contains the combined data3. This can help simplify the data management and governance, as well as improve the data quality and consistency.Reference:

Databricks SQL overview

Databricks SQL use cases

Joining data sources


Question No. 3

A data organization has a team of engineers developing data pipelines following the medallion architecture using Delta Live Tables. While the data analysis team working on a project is using gold-layer tables from these pipelines, they need to perform some additional processing of these tables prior to performing their analysis.

Which of the following terms is used to describe this type of work?

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Correct Answer: D

Last-mile ETL is the term used to describe the additional processing of data that is done by data analysts or data scientists after the data has been ingested, transformed, and stored in the lakehouse by data engineers. Last-mile ETL typically involves tasks such as data cleansing, data enrichment, data aggregation, data filtering, or data sampling that are specific to the analysis or machine learning use case. Last-mile ETL can be done using Databricks SQL, Databricks notebooks, or Databricks Machine Learning.Reference:Databricks - Last-mile ETL,Databricks - Data Analysis with Databricks SQL


Question No. 4

Which of the following statements describes descriptive statistics?

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Correct Answer: A

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


Question No. 5

In which of the following situations will the mean value and median value of variable be meaningfully different?

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Correct Answer: E

The mean value of a variable is the average of all the values in a data set, calculated by dividing the sum of the values by the number of values. The median value of a variable is the middle value of the ordered data set, or the average of the middle two values if the data set has an even number of values. The mean value is sensitive to outliers, which are values that are very different from the rest of the data. Outliers can skew the mean value and make it less representative of the central tendency of the data. The median value is more robust to outliers, as it only depends on the middle values of the data.Therefore, when the variable contains a lot of extreme outliers, the mean value and the median value will be meaningfully different, as the mean value will be pulled towards the outliers, while the median value will remain close to the majority of the data1.Reference:Difference Between Mean and Median in Statistics (With Example) - BYJU'S


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