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2025 Databricks-Certified-Data-Analyst-Associate Question Explanations | Efficient 100% Free Flexible Databricks Certified Data Analyst Associate Exam Testing Engine

2025 Databricks-Certified-Data-Analyst-Associate Question Explanations | Efficient 100% Free Flexible Databricks Certified Data Analyst Associate Exam Testing Engine

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
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. IBar Chart
  • E. There is no default. The user must choose a visualization type.

Answer: D

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 # 13
In which of the following situations will the mean value and median value of variable be meaningfully different?

  • A. When the variable is of the boolean type
  • B. When the variable contains no missing values
  • C. When the variable is of the categorical type
  • D. When the variable contains no outliers
  • E. When the variable contains a lot of extreme outliers

Answer: E

Explanation:
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


NEW QUESTION # 14
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. Alerts don't work with queries that access tables.
  • B. Queries that use query parameters cannot be used with Alerts.
  • C. The wrong query parameter is being used. Alerts only work with Date and Time query parameters.
  • D. Queries that return results based on dates cannot be used with Alerts.
  • E. The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.

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 # 15
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 enhancement
  • B. Last-mile
  • C. Last-mile ETL
  • D. Data testing
  • E. Ad-hoc improvements

Answer: A

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 # 16
Which of the following is a benefit of Databricks SQL using ANSI SQL as its standard SQL dialect?

  • A. It is more compatible with Spark's interpreters
  • B. It is easy to migrate existing SQL queries to Databricks SQL
  • C. It is more performant than other SQL dialects
  • D. It allows for the use of Photon's computation optimizations
  • E. It has increased customization capabilities

Answer: B

Explanation:
Databricks SQL uses ANSI SQL as its standard SQL dialect, which means it follows the SQL specifications defined by the American National Standards Institute (ANSI). This makes it easier to migrate existing SQL queries from other data warehouses or platforms that also use ANSI SQL or a similar dialect, such as PostgreSQL, Oracle, or Teradata. By using ANSI SQL, Databricks SQL avoids surprises in behavior or unfamiliar syntax that may arise from using a non-standard SQL dialect, such as Spark SQL or Hive SQL12. Moreover, Databricks SQL also adds compatibility features to support common SQL constructs that are widely used in other data warehouses, such as QUALIFY, FILTER, and user-defined functions2. Reference: ANSI compliance in Databricks Runtime, Evolution of the SQL language at Databricks: ANSI standard by default and easier migrations from data warehouses


NEW QUESTION # 17
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