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Databricks Certified Data Analyst Associate Exam Certification Sample Questions and Practice Exam

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
In which circumstance will there be a substantial difference between the variable's mean and median values?

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

Answer: A

Explanation:
The mean is sensitive to extreme values, often called outliers, which can significantly skew the average away from the true center of the data. The median, however, is a measure of central tendency that is resistant to such outliers because it only considers the middle value(s) when the data is ordered. Therefore, when a variable contains many extreme outliers, there will be a substantial difference between the mean and the median. According to Databricks data analysis materials, this is a fundamental concept when choosing summary statistics for reporting.

NEW QUESTION # 21
A data analyst has created a Query in Databricks SQL, and now they want to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which of the following steps will they need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?

A. They will need to alter the Query to return two separate sets of results.
B. They will need to create two separate dashboards.
C. They will need to add two separate visualizations to the dashboard based on the same Query.
D. They will need to copy the Query and create one data visualization per query.
E. They will need to decide on a single data visualization to add to the dashboard.

Answer: C

Explanation:
A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard. Reference: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL

NEW QUESTION # 22
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?

A. Testing the quality of data as it is imported from a source
B. Combining two data sources into a single, comprehensive dataset
C. Tracking usage of feature variables for machine learning projects
D. Segmenting customers into like groups using a clustering algorithm
E. Automating complex notebook-based workflows with multiple tasks

Answer: B

Explanation:
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

NEW QUESTION # 23
A data analyst runs the following command:
INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers;
What is the result of running this command?

A. The suppliers table now contains the data from the new suppliers table, and the new suppliers table now contains the data from the suppliers table.
B. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, including any duplicate data.
C. The suppliers table now contains only the data from the new suppliers table.
D. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, and any duplicate data is deleted.
E. The command fails because it is written incorrectly.

Answer: E

Explanation:
The command INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers is not a valid syntax for inserting data into a table in Databricks SQL. According to the documentation12, the correct syntax for inserting data into a table is either:
INSERT { OVERWRITE | INTO } [ TABLE ] table_name [ PARTITION clause ] [ ( column_name [, ...] ) | BY NAME ] query INSERT INTO [ TABLE ] table_name REPLACE WHERE predicate query The command in the question is missing the OVERWRITE or INTO keyword, and the query part that specifies the source of the data to be inserted. The TABLE keyword is optional and can be omitted. The PARTITION clause and the column list are also optional and depend on the table schema and the data source. Therefore, the command in the question will fail with a syntax error.
Reference:
INSERT | Databricks on AWS
INSERT - Azure Databricks - Databricks SQL | Microsoft Learn

NEW QUESTION # 24
Which of the following is an advantage of using a Delta Lake-based data lakehouse over common data lake solutions?

A. Open-source formats
B. Flexible schemas
C. Data deletion
D. Scalable storage
E. ACID transactions

Answer: E

Explanation:
A Delta Lake-based data lakehouse is a data platform architecture that combines the scalability and flexibility of a data lake with the reliability and performance of a data warehouse. One of the key advantages of using a Delta Lake-based data lakehouse over common data lake solutions is that it supports ACID transactions, which ensure data integrity and consistency. ACID transactions enable concurrent reads and writes, schema enforcement and evolution, data versioning and rollback, and data quality checks. These features are not available in traditional data lakes, which rely on file-based storage systems that do not support transactions. Reference:
Delta Lake: Lakehouse, warehouse, advantages | Definition
Synapse - Data Lake vs. Delta Lake vs. Data Lakehouse
Data Lake vs. Delta Lake - A Detailed Comparison
Building a Data Lakehouse with Delta Lake Architecture: A Comprehensive Guide

NEW QUESTION # 25
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