Free Databricks Databricks-Certified-Data-Analyst-Associate Exam Questions & Answer from Training Expert TroytecDumps [Q35-Q56]

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Free Databricks Databricks-Certified-Data-Analyst-Associate Exam Questions and Answer from Training Expert TroytecDumps

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QUESTION 35
Which statement about subqueries is correct?

Subqueries are not available in Databricks SQL

Subqueries can be used like other user-defined functions to transform data into different data types.

Subqueries can retrieve data without requiring the creation of a table or view.

Subqueries can be used like other built-in functions to transform data into different data types.

QUESTION 36
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

QUESTION 37
Which statement describes descriptive statistics?

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 summary statistics to categorically describe and summarize data.

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

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

QUESTION 38
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 a complete replacement with additional functionality

As a complementary tool for quick in-platform Bl work

As a substitute with less functionality

As an exact substitute with the same level of functionality

As a complementary tool for professional-grade presentations

QUESTION 39
Which of the following statements about a refresh schedule is incorrect?

A query can be refreshed anywhere from 1 minute lo 2 weeks

Refresh schedules can be configured in the Query Editor.

A query being refreshed on a schedule does not use a SQL Warehouse (formerly known as SQL Endpoint).

A refresh schedule is not the same as an alert.

You must have workspace administrator privileges to configure a refresh schedule

QUESTION 40
A data analyst has been asked to provide a list of options on how to share a dashboard with a client. It is a security requirement that the client does not gain access to any other information, resources, or artifacts in the database.
Which of the following approaches cannot be used to share the dashboard and meet the security requirement?

Download the Dashboard as a PDF and share it with the client.

Set a refresh schedule for the dashboard and enter the client’s email address in the “Subscribers” box.

Take a screenshot of the dashboard and share it with the client.

Generate a Personal Access Token that is good for 1 day and share it with the client.

Download a PNG file of the visualizations in the dashboard and share them with the client.

QUESTION 41
A data analyst has a managed table table_name in database database_name. They would now like to remove the table from the database and all of the data files associated with the table. The rest of the tables in the database must continue to exist.
Which of the following commands can the analyst use to complete the task without producing an error?

DROP DATABASE database_name;

DROP TABLE database_name.table_name;

DELETE TABLE database_name.table_name;

DELETE TABLE table_name FROM database_name;

DROP TABLE table_name FROM database_name;

QUESTION 42
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

QUESTION 43
A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every 10 minutes.
A data analyst has created a dashboard based on this gold level dat
a. The project stakeholders want to see the results in the dashboard updated within 10 minutes or less of new data becoming available within the gold-level tables.
What is the ability to ensure the streamed data is included in the dashboard at the standard requested by the project stakeholders?

A refresh schedule with an interval of 10 minutes or less

A refresh schedule with an always-on SQL Warehouse (formerly known as SQL Endpoint

A refresh schedule with stakeholders included as subscribers

A refresh schedule with a Structured Streaming cluster

QUESTION 44
A data analyst has created a user-defined function using the following line of code:
CREATE FUNCTION price(spend DOUBLE, units DOUBLE)
RETURNS DOUBLE
RETURN spend / units;
Which of the following code blocks can be used to apply this function to the customer_spend and customer_units columns of the table customer_summary to create column customer_price?

SELECT PRICE customer_spend, customer_units AS customer_price FROM customer_summary

SELECT price FROM customer_summary

SELECT function(price(customer_spend, customer_units)) AS customer_price FROM customer_summary

SELECT double(price(customer_spend, customer_units)) AS customer_price FROM customer_summary

SELECT price(customer_spend, customer_units) AS customer_price FROM customer_summary

QUESTION 45
What is a benefit of using Databricks SQL for business intelligence (Bl) analytics projects instead of using third-party Bl tools?

Computations, data, and analytical tools on the same platform

Advanced dashboarding capabilities

Simultaneous multi-user support

Automated alerting systems

QUESTION 46
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

QUESTION 47
A stakeholder has provided a data analyst with a lookup dataset in the form of a 50-row CSV file. The data analyst needs to upload this dataset for use as a table in Databricks SQL.
Which approach should the data analyst use to quickly upload the file into a table for use in Databricks SOL?

Create a table by uploading the file using the Create page within Databricks SQL

Create a table via a connection between Databricks and the desktop facilitated by Partner Connect.

Create a table by uploading the file to cloud storage and then importing the data to Databricks.

Create a table by manually copying and pasting the data values into cloud storage and then importing the data to Databricks.

QUESTION 48
A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every minute.
A data analyst has created a dashboard based on this gold-level data. The project stakeholders want to see the results in the dashboard updated within one minute or less of new data becoming available within the gold-level tables.
Which of the following cautions should the data analyst share prior to setting up the dashboard to complete this task?

The required compute resources could be costly

The gold-level tables are not appropriately clean for business reporting

The streaming data is not an appropriate data source for a dashboard

The streaming cluster is not fault tolerant

The dashboard cannot be refreshed that quickly

A Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables every minute requires a high level of compute resources to handle the frequent data ingestion, processing, and writing. This could result in a significant cost for the organization, especially if the data volume and velocity are large. Therefore, the data analyst should share this caution with the project stakeholders before setting up the dashboard and evaluate the trade-offs between the desired refresh rate and the available budget. The other options are not valid cautions because:
B) The gold-level tables are assumed to be appropriately clean for business reporting, as they are the final output of the data engineering pipeline. If the data quality is not satisfactory, the issue should be addressed at the source or silver level, not at the gold level.
C) The streaming data is an appropriate data source for a dashboard, as it can provide near real-time insights and analytics for the business users. Structured Streaming supports various sources and sinks for streaming data, including Delta Lake, which can enable both batch and streaming queries on the same data.
D) The streaming cluster is fault tolerant, as Structured Streaming provides end-to-end exactly-once fault-tolerance guarantees through checkpointing and write-ahead logs. If a query fails, it can be restarted from the last checkpoint and resume processing.
E) The dashboard can be refreshed within one minute or less of new data becoming available in the gold-level tables, as Structured Streaming can trigger micro-batches as fast as possible (every few seconds) and update the results incrementally. However, this may not be necessary or optimal for the business use case, as it could cause frequent changes in the dashboard and consume more resources. Reference: Streaming on Databricks, Monitoring Structured Streaming queries on Databricks, A look at the new Structured Streaming UI in Apache Spark 3.0, Run your first Structured Streaming workload

QUESTION 49
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 NULL.

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.

Based on the images you sent, the two statements are SQL queries for different types of joins between the customers and orders tables. A join is a way of combining the rows from two table references based on some criteria. The join type determines how the rows are matched and what kind of result set is returned. The first statement is a query for a LEFT SEMI JOIN, which returns only the rows from the left table reference (customers) that have a match with the right table reference (orders) on the join condition (customer_id). The second statement is a query for a LEFT ANTI JOIN, which returns only the rows from the left table reference (customers) that have no match with the right table reference (orders) on the join condition (customer_id). Therefore, the result sets for the two statements will differ in the following way:
The first statement will return a subset of the customers table that contains only the customers who have placed at least one order. The number of rows returned will be less than or equal to the number of rows in the customers table, depending on how many customers have orders. The number of columns returned will be the same as the number of columns in the customers table, as the LEFT SEMI JOIN does not include any columns from the orders table.
The second statement will return a subset of the customers table that contains only the customers who have not placed any order. The number of rows returned will be less than or equal to the number of rows in the customers table, depending on how many customers have no orders. The number of columns returned will be the same as the number of columns in the customers table, as the LEFT ANTI JOIN does not include any columns from the orders table.
The other options are not correct because:
A) The first statement will not return all data from the customers table, as it will exclude the customers who have no orders. The second statement will not return all data from the orders table, as it will exclude the orders that have a matching customer. Neither statement will fill in any missing data with NULL, as they do not return any columns from the other table.
C) There is a difference between the result sets for both statements, as explained above. The LEFT SEMI JOIN and the LEFT ANTI JOIN are not equivalent operations and will produce different outputs.
D) Both statements will not fail, as Databricks SQL does support those join types. Databricks SQL supports various join types, including INNER, LEFT OUTER, RIGHT OUTER, FULL OUTER, LEFT SEMI, LEFT ANTI, and CROSS. You can also use NATURAL, USING, or LATERAL keywords to specify different join criteria.
E) The first statement will not return only the customer_id from the orders table, as it will return all columns from the customers table. The second statement is correct, but it is not the only difference between the result sets.

QUESTION 50
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?

Alerts don’t work with queries that access tables.

Queries that return results based on dates cannot be used with Alerts.

The wrong query parameter is being used. Alerts only work with Date and Time query parameters.

Queries that use query parameters cannot be used with Alerts.

The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.

QUESTION 51
A data analyst has been asked to count the number of customers in each region and has written the following query:

If there is a mistake in the query, which of the following describes the mistake?

The query is using count(‘). which will count all the customers in the customers table, no matter the region.

The query is missing a GROUP BY region clause.

The query is using ORDER BY. which is not allowed in an aggregation.

There are no mistakes in the query.

The query is selecting region but region should only occur in the ORDER BY clause.

QUESTION 52
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

QUESTION 53
A data analyst created and is the owner of the managed table my_ table. They now want to change ownership of the table to a single other user using Data Explorer.
Which of the following approaches can the analyst use to complete the task?

Edit the Owner field in the table page by removing their own account

Edit the Owner field in the table page by selecting All Users

Edit the Owner field in the table page by selecting the new owner’s account

Edit the Owner field in the table page by selecting the Admins group

Edit the Owner field in the table page by removing all access

QUESTION 54
Which of the following approaches can be used to connect Databricks to Fivetran for data ingestion?

Use Workflows to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with

Use Delta Live Tables to establish a cluster for Fivetran to interact with

Use Partner Connect’s automated workflow to establish a cluster for Fivetran to interact with

Use Partner Connect’s automated workflow to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with

Use Workflows to establish a cluster for Fivetran to interact with

QUESTION 55
A data analyst created and is the owner of the managed table my_ table. They now want to change ownership of the table to a single other user using Data Explorer.
Which of the following approaches can the analyst use to complete the task?

Edit the Owner field in the table page by selecting the Admins group

Edit the Owner field in the table page by removing their own account

Edit the Owner field in the table page by selecting the new owner’s account

Edit the Owner field in the table page by removing all access

Edit the Owner field in the table page by selecting All Users

QUESTION 56
A data analyst has been asked to produce a visualization that shows the flow of users through a website.
Which of the following is used for visualizing this type of flow?

Heatmap

IChoropleth

Word Cloud

Pivot Table

Sankey

Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	SQL in the Lakehouse: It identifies a query that retrieves data from the database, the output of a SELECT query, a benefit of having ANSI SQL, access, and clean silver-level data. It also compares and contrasts MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
Topic 2	Data Management: The topic describes Delta Lake as a tool for managing data files, Delta Lake manages table metadata, benefits of Delta Lake within the Lakehouse, tables on Databricks, a table owner’s responsibilities, and the persistence of data. It also identifies management of a table, usage of Data Explorer by a table owner, and organization-specific considerations of PII data. Lastly, the topic it explains how the LOCATION keyword changes, usage of Data Explorer to secure data.
Topic 3	Data Visualization and Dashboarding: Sub-topics of this topic are about of describing how notifications are sent, how to configure and troubleshoot a basic alert, how to configure a refresh schedule, the pros and cons of sharing dashboards, how query parameters change the output, and how to change the colors of all of the visualizations. It also discusses customized data visualizations, visualization formatting, Query Based Dropdown List, and the method for sharing a dashboard.
Topic 4	Databricks SQL: This topic discusses key and side audiences, users, Databricks SQL benefits, complementing a basic Databricks SQL query, schema browser, Databricks SQL dashboards, and the purpose of Databricks SQL endpoints warehouses. Furthermore, the delves into Serverless Databricks SQL endpoint warehouses, trade-off between cluster size and cost for Databricks SQL endpoints warehouses, and Partner Connect. Lastly it discusses small-file upload, connecting Databricks SQL to visualization tools, the medallion architecture, the gold layer, and the benefits of working with streaming data.
Topic 5	Analytics applications: It describes key moments of statistical distributions, data enhancement, and the blending of data between two source applications. Moroever, the topic also explains last-mile ETL, a scenario in which data blending would be beneficial, key statistical measures, descriptive statistics, and discrete and continuous statistics.

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Free Databricks Databricks-Certified-Data-Analyst-Associate Exam Questions & Answer from Training Expert TroytecDumps [Q35-Q56]

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