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Databricks Certified Data Analyst Associate Exam Sample Questions (Q57-Q62):
NEW QUESTION # 57
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 a variety of data analysis techniques to infer properties of an underlying distribution of probability.
- C. A branch of statistics that uses quantitative variables that must take on an uncountable set of values.
- D. A branch of statistics that uses summary statistics to categorically describe and summarize data.
- E. A branch of statistics that uses summary statistics to quantitatively describe and summarize data.
Answer: E
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 # 58
A data analysis team is working with the table_bronze SQL table as a source for one of its most complex projects. A stakeholder of the project notices that some of the downstream data is duplicative. The analysis team identifies table_bronze as the source of the duplication.
Which of the following queries can be used to deduplicate the data from table_bronze and write it to a new table table_silver?
A)
CREATE TABLE table_silver AS
SELECT DISTINCT *
FROM table_bronze;
B)
CREATE TABLE table_silver AS
INSERT *
FROM table_bronze;
C)
CREATE TABLE table_silver AS
MERGE DEDUPLICATE *
FROM table_bronze;
D)
INSERT INTO TABLE table_silver
SELECT * FROM table_bronze;
E)
INSERT OVERWRITE TABLE table_silver
SELECT * FROM table_bronze;
- A. Option A
- B. Option E
- C. Option C
- D. Option D
- E. Option B
Answer: A
Explanation:
Option A uses the SELECT DISTINCT statement to remove duplicate rows from the table_bronze and create a new table table_silver with the deduplicated data. This is the correct way to deduplicate data using Spark SQL12. Option B simply inserts all the rows from table_bronze into table_silver, without removing any duplicates. Option C is not a valid syntax for Spark SQL, as there is no MERGE DEDUPLICATE statement. Option D appends all the rows from table_bronze into table_silver, without removing any duplicates. Option E overwrites the existing data in table_silver with the data from table_bronze, without removing any duplicates. Reference: Delete Duplicate using SPARK SQL, Spark SQL - How to Remove Duplicate Rows
NEW QUESTION # 59
Which of the following describes how Databricks SQL should be used in relation to other business intelligence (BI) tools like Tableau, Power BI, and looker?
- A. As an exact substitute with the same level of functionality
- B. As a substitute with less functionality
- C. As a complete replacement with additional functionality
- D. As a complementary tool for quick in-platform Bl work
- E. As a complementary tool for professional-grade presentations
Answer: D
Explanation:
Databricks SQL is not meant to replace or substitute other BI tools, but rather to complement them by providing a fast and easy way to query, explore, and visualize data on the lakehouse using the built-in SQL editor, visualizations, and dashboards. Databricks SQL also integrates seamlessly with popular BI tools like Tableau, Power BI, and Looker, allowing analysts to use their preferred tools to access data through Databricks clusters and SQL warehouses. Databricks SQL offers low-code and no-code experiences, as well as optimized connectors and serverless compute, to enhance the productivity and performance of BI workloads on the lakehouse. Reference: Databricks SQL, Connecting Applications and BI Tools to Databricks SQL, Databricks integrations overview, Databricks SQL: Delivering a Production SQL Development Experience on the Lakehouse
NEW QUESTION # 60
An analyst writes a query that contains a query parameter. They then add an area chart visualization to the query. While adding the area chart visualization to a dashboard, the analyst chooses "Dashboard Parameter" for the query parameter associated with the area chart.
Which of the following statements is true?
- A. The area chart will use whatever is selected in the Dashboard Parameter along with all of the other visualizations in the dashboard that use the same parameter.
- B. The area chart will use whatever value is chosen on the dashboard at the time the area chart is added to the dashboard.
- C. The area chart will use whatever value is input by the analyst when the visualization is added to the dashboard. The parameter cannot be changed by the user afterwards.
- D. The area chart will convert to a Dashboard Parameter.
- E. The area chart will use whatever is selected in the Dashboard Parameter while all or the other visualizations will remain changed regardless of their parameter use.
Answer: A
Explanation:
A Dashboard Parameter is a parameter that is configured for one or more visualizations within a dashboard and appears at the top of the dashboard. The parameter values specified for a Dashboard Parameter apply to all visualizations reusing that particular Dashboard Parameter1. Therefore, if the analyst chooses "Dashboard Parameter" for the query parameter associated with the area chart, the area chart will use whatever is selected in the Dashboard Parameter along with all of the other visualizations in the dashboard that use the same parameter. This allows the user to filter the data across multiple visualizations using a single parameter widget2. Reference: Databricks SQL dashboards, Query parameters
NEW QUESTION # 61
Which of the following layers of the medallion architecture is most commonly used by data analysts?
- A. All of these layers are used equally by data analysts
- B. Bronze
- C. None of these layers are used by data analysts
- D. Silver
- E. Gold
Answer: E
Explanation:
The gold layer of the medallion architecture contains data that is highly refined and aggregated, and powers analytics, machine learning, and production applications. Data analysts typically use the gold layer to access data that has been transformed into knowledge, rather than just information. The gold layer represents the final stage of data quality and optimization in the lakehouse. Reference: What is the medallion lakehouse architecture?
NEW QUESTION # 62
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