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Verified Databricks-Certified-Data-Analyst-Associate exam dumps Q&As with Correct 67 Questions and Answers

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 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 3	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 4	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.
Topic 5	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.

QUESTION 26
In which circumstance will there be a substantial difference between the variable’s mean and median values?

When the variable is of the categorical type

When the variable is of the boolean type

When the variable contains no outliers

When the variable contains a lot of extreme outliers

QUESTION 27
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 28
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?

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.

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.

The area chart will use whatever value is chosen on the dashboard at the time the area chart is added to the dashboard.

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.

The area chart will convert to a Dashboard Parameter.

QUESTION 29
Delta Lake stores table data as a series of data files, but it also stores a lot of other information.
Which of the following is stored alongside data files when using Delta Lake?

None of these

Table metadata, data summary visualizations, and owner account information

Table metadata

Data summary visualizations

Owner account information

QUESTION 30
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 31
Which of the following is an advantage of using a Delta Lake-based data lakehouse over common data lake solutions?

ACID transactions

Flexible schemas

Data deletion

Scalable storage

Open-source formats

QUESTION 32
The stakeholders.customers table has 15 columns and 3,000 rows of data. The following command is run:

After running SELECT * FROM stakeholders.eur_customers, 15 rows are returned. After the command executes completely, the user logs out of Databricks.
After logging back in two days later, what is the status of the stakeholders.eur_customers view?

The view remains available and SELECT * FROM stakeholders.eur_customers will execute correctly.

The view has been dropped.

The view is not available in the metastore, but the underlying data can be accessed with SELECT * FROM delta. `stakeholders.eur_customers`.

The view remains available but attempting to SELECT from it results in an empty result set because data in views are automatically deleted after logging out.

The view has been converted into a table.

QUESTION 33
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 34
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.

QUESTION 35
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 36
Which of the following is a benefit of Databricks SQL using ANSI SQL as its standard SQL dialect?

It has increased customization capabilities

It is easy to migrate existing SQL queries to Databricks SQL

It allows for the use of Photon’s computation optimizations

It is more performant than other SQL dialects

It is more compatible with Spark’s interpreters

QUESTION 37
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 38
In which of the following situations will the mean value and median value of variable be meaningfully different?

When the variable contains no outliers

When the variable contains no missing values

When the variable is of the boolean type

When the variable is of the categorical type

When the variable contains a lot of extreme outliers

QUESTION 39
A data analyst is attempting to drop a table my_table. The analyst wants to delete all table metadata and data.
They run the following command:
DROP TABLE IF EXISTS my_table;
While the object no longer appears when they run SHOW TABLES, the data files still exist.
Which of the following describes why the data files still exist and the metadata files were deleted?

The table’s data was larger than 10 GB

The table did not have a location

The table was external

The table’s data was smaller than 10 GB

The table was managed

QUESTION 40
In which of the following situations should a data analyst use higher-order functions?

When custom logic needs to be applied to simple, unnested data

When custom logic needs to be converted to Python-native code

When custom logic needs to be applied at scale to array data objects

When built-in functions are taking too long to perform tasks

When built-in functions need to run through the Catalyst Optimizer

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