Databricks Certified Data Analyst Associate Dumps Questions – Effective Way to Get Certified

If you're in the field of Databricks, you know how important it is to stay up-to-date with the latest knowledge and skills to protect your organization's networks and data. One way to do that is by obtaining Data Analyst, specifically the Databricks Certified Data Analyst Associate exam. While preparing for the Databricks Certified Data Analyst Associate exam, you might consider using Databricks Certified Data Analyst Associate dumps to help you familiarize yourself with the exam format and content. These Databricks Certified Data Analyst Associate exam dumps questions can be an effective way to gauge your knowledge and identify areas where you may need additional study. Study online free Databricks Certified Data Analyst Associate exam dumps below.

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1. Which of the following layers of the medallion architecture is most commonly used by data analysts?

None of these layers are used by data analysts

Gold

All of these layers are used equally by data analysts

Silver

Bronze

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2. Which of the following statements describes descriptive statistics?

A branch of statistics that uses summary statistics to quantitatively describe and summarize data.

A branch of statistics that uses a variety of data analysis techniques to infer properties of an underlying distribution of probability.

A branch of statistics that uses quantitative variables that must take on a finite or countably infinite set of values.

A branch of statistics that uses summary statistics to categorically describe and summarize data.

A branch of statistics that uses quantitative variables that must take on an uncountable set of values.

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3. Consider the following two statements:

Statement 1:





Statement 2:





Which of the following describes how the result sets will differ for each statement when they are run in Databricks SQL?

The first statement will return all data from the customers table and matching data from the orders table. The second statement will return all data from the orders table and matching data from the customers table. Any missing data will be filled in with NUL

When the first statement is run, only rows from the customers table that have at least one match with the orders table on customer_id will be returned. When the second statement is run, only those rows in the customers table that do not have at least one match with the orders table on customer_id will be returned.

There is no difference between the result sets for both statements.

Both statements will fail because Databricks SQL does not support those join types.

When the first statement is run, all rows from the customers table will be returned and only the customer_id from the orders table will be returned. When the second statement is run, only those rows in the customers table that do not have at least one match with the orders table on customer_id will be returned.

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4. 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?

Data testing

Ad-hoc improvements

Last-mile

Last-mile ETL

Data enhancement

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5. 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?

They will need to alter the Query to return two separate sets of results.

They will need to add two separate visualizations to the dashboard based on the same Query.

They will need to create two separate dashboards.

They will need to decide on a single data visualization to add to the dashboard.

They will need to copy the Query and create one data visualization per query.

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6. Which of the following should data analysts consider when working with personally identifiable information (PII) data?

Organization-specific best practices for Pll data

Legal requirements for the area in which the data was collected

None of these considerations

Legal requirements for the area in which the analysis is being performed

All of these considerations

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7. 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?

Data blending

Last-mile

Data testing

Last-mile ETL

Data enhancement

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8. 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?

Testing the quality of data as it is imported from a source

Tracking usage of feature variables for machine learning projects

Combining two data sources into a single, comprehensive dataset

Segmenting customers into like groups using a clustering algorithm

Automating complex notebook-based workflows with multiple tasks

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9. A data analyst has set up a SQL query to run every four hours on a SQL endpoint, but the SQL endpoint is taking too long to start up with each run.

Which of the following changes can the data analyst make to reduce the start-up time for the endpoint while managing costs?

Reduce the SQL endpoint cluster size

Increase the SQL endpoint cluster size

Turn off the Auto stop feature

Increase the minimum scaling value

Use a Serverless SQL endpoint

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10. A data analyst has been asked to use the below table sales_table to get the percentage rank of products within region by the sales:





The result of the query should look like this:





Which of the following queries will accomplish this task?

A)





B)





C)





D)

Option A

Option B

Option C

Option D

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11. A data analyst runs the following command:

INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers;

What is the result of running this command?

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.

The command fails because it is written incorrectly.

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.

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.

The suppliers table now contains only the data from the new suppliers table.

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12. 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?

As an exact substitute with the same level of functionality

As a substitute with less functionality

As a complete replacement with additional functionality

As a complementary tool for professional-grade presentations

As a complementary tool for quick in-platform Bl work

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13. After running DESCRIBE EXTENDED accounts.customers;, the following was returned:





Now, a data analyst runs the following command:

DROP accounts.customers;

Which of the following describes the result of running this command?

Running SELECT * FROM delta. `dbfs:/stakeholders/customers` results in an error.

Running SELECT * FROM accounts.customers will return all rows in the table.

All files with the .customers extension are deleted.

The accounts.customers table is removed from the metastore, and the underlying data files are deleted.

The accounts.customers table is removed from the metastore, but the underlying data files are untouched.

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14. A data engineer is working with a nested array column products in table transactions. They want to expand the table so each unique item in products for each row has its own row where the transaction_id column is duplicated as necessary.

They are using the following incomplete command:





Which of the following lines of code can they use to fill in the blank in the above code block so that it successfully completes the task?

array distinct(produces)

explode(produces)

reduce(produces)

array(produces)

flatten(produces)

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