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Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:
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NEW QUESTION # 20
Which of the following benefits of using Databricks SQL is provided by Data Explorer?
- A. It can be used to view metadata and data, as well as view/change permissions.
- B. It can be used to connect to third party Bl cools.
- C. It can be used to run UPDATE queries to update any tables in a database.
- D. It can be used to produce dashboards that allow data exploration.
- E. It can be used to make visualizations that can be shared with stakeholders.
Answer: A
Explanation:
Data Explorer is a user interface that allows you to discover and manage data, schemas, tables, models, and permissions in Databricks SQL. You can use Data Explorer to view schema details, preview sample data, and see table and model details and properties. Administrators can view and change owners, and admins and data object owners can grant and revoke permissions1. Reference: Discover and manage data using Data Explorer
NEW QUESTION # 21
Which statement describes descriptive statistics?
- A. A branch of statistics that uses summary statistics to categorically describe and summarize data.
- B. A branch of statistics that uses summary statistics to quantitatively describe and summarize data.
- C. A branch of statistics that uses a variety of data analysis techniques to infer properties of an underlying distribution of probability.
- D. A branch of statistics that uses quantitative variables that must take on a finite or countably infinite set of values.
Answer: B
NEW QUESTION # 22
Which of the following statements about adding visual appeal to visualizations in the Visualization Editor is incorrect?
- A. Visualization scale can be changed.
- B. Borders can be added.
- C. Colors can be changed.
- D. Data Labels can be formatted.
- E. Tooltips can be formatted.
Answer: B
Explanation:
The Visualization Editor in Databricks SQL allows users to create and customize various types of charts and visualizations from the query results. Users can change the visualization type, select the data fields, adjust the colors, format the data labels, and modify the tooltips. However, there is no option to add borders to the visualizations in the Visualization Editor. Borders are not a supported feature of the new chart visualizations in Databricks1. Therefore, the statement that borders can be added is incorrect. Reference:
New chart visualizations in Databricks | Databricks on AWS
NEW QUESTION # 23
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?
- A. Word Cloud
- B. Sankey
- C. IChoropleth
- D. Pivot Table
- E. Heatmap
Answer: B
Explanation:
A Sankey diagram is a type of visualization that shows the flow of data between different nodes or categories. It is often used to represent the movement of users through a website, as it can show the paths they take, the sources they come from, the pages they visit, and the outcomes they achieve. A Sankey diagram consists of links and nodes, where the links represent the volume or weight of the flow, and the nodes represent the stages or steps of the flow. The width of the links is proportional to the amount of flow, and the color of the links can indicate different attributes or segments of the flow. A Sankey diagram can help identify the most common or popular user journeys, the bottlenecks or drop-offs in the flow, and the opportunities for improvement or optimization. Reference: The answer can be verified from Databricks documentation which provides examples and instructions on how to create Sankey diagrams using Databricks SQL Analytics and Databricks Visualizations. Reference links: Databricks SQL Analytics - Sankey Diagram, Databricks Visualizations - Sankey Diagram
NEW QUESTION # 24
A data analyst creates a Databricks SQL Query where the result set has the following schema:
region STRING
number_of_customer INT
When the analyst clicks on the "Add visualization" button on the SQL Editor page, which of the following types of visualizations will be selected by default?
- A. IBar Chart
- B. Histogram
- C. Violin Chart
- D. There is no default. The user must choose a visualization type.
- E. Line Chart
Answer: A
Explanation:
According to the Databricks SQL documentation, when a data analyst clicks on the "Add visualization" button on the SQL Editor page, the default visualization type is Bar Chart. This is because the result set has two columns: one of type STRING and one of type INT. The Bar Chart visualization automatically assigns the STRING column to the X-axis and the INT column to the Y-axis. The Bar Chart visualization is suitable for showing the distribution of a numeric variable across different categories. Reference: Visualization in Databricks SQL, Visualization types
NEW QUESTION # 25
Which statement about subqueries is correct?
- A. Subqueries can retrieve data without requiring the creation of a table or view.
- B. Subqueries can be used like other user-defined functions to transform data into different data types.
- C. Subqueries are not available in Databricks SQL
- D. Subqueries can be used like other built-in functions to transform data into different data types.
Answer: A
Explanation:
In Databricks SQL, a subquery is a nested query within a larger SQL query that allows for the retrieval of data without the necessity of creating a table or view. This is particularly useful for simplifying complex queries by breaking them down into more manageable parts. Subqueries can be employed in various clauses such as SELECT, FROM, and WHERE to perform operations like filtering, transforming, and aggregating data on-the-fly. This flexibility enhances query efficiency and readability without the overhead of persisting intermediate results as separate tables or views.
NEW QUESTION # 26
A data analyst wants to create a Databricks SQL dashboard with multiple data visualizations and multiple counters. What must be completed before adding the data visualizations and counters to the dashboard?
- A. All data visualizations and counters must be created using Queries.
- B. A SQL warehouse (formerly known as SQL endpoint) must be turned on and selected.
- C. The dashboard owner must also be the owner of the queries, data visualizations, and counters.
- D. A markdown-based tile must be added to the top of the dashboard displaying the dashboard's name.
Answer: A
Explanation:
In Databricks SQL, when creating a dashboard that includes multiple data visualizations and counters, it is imperative that each visualization and counter is based on a query. The process involves the following steps:
Develop Queries:
For each desired visualization or counter, write a SQL query that retrieves the necessary data.
Create Visualizations and Counters:
After executing each query, utilize the results to create corresponding visualizations or counters. Databricks SQL offers a variety of visualization types to represent data effectively.
Assemble the Dashboard:
Add the created visualizations and counters to your dashboard, arranging them as needed to convey the desired insights.
By ensuring that all components of the dashboard are derived from queries, you maintain consistency, accuracy, and the ability to refresh data as needed. This approach also facilitates easier maintenance and updates to the dashboard elements.
NEW QUESTION # 27
Which of the following statements about a refresh schedule is incorrect?
- A. A query being refreshed on a schedule does not use a SQL Warehouse (formerly known as SQL Endpoint).
- B. A query can be refreshed anywhere from 1 minute lo 2 weeks
- C. Refresh schedules can be configured in the Query Editor.
- D. You must have workspace administrator privileges to configure a refresh schedule
- E. A refresh schedule is not the same as an alert.
Answer: D
Explanation:
This statement is incorrect. In Databricks SQL, any user with sufficient permissions on the query or dashboard can configure a refresh schedule-workspace administrator privileges are not required.
Here is the breakdown of the correct information:
A . True - Queries can be scheduled to refresh at intervals ranging from 1 minute to 2 weeks.
B . True - You can configure refresh schedules in the Query Editor.
C . False statement - A query being refreshed does use a SQL Warehouse. However, the option in question says it does not use a warehouse, which would be incorrect in a different context. Since this is a trickier one, we know that scheduled queries do require a SQL Warehouse to run.
D . True - Refresh schedules are different from alerts; alerts are triggered based on specific conditions being met in query results.
E . False (and thus the correct answer to this question) - You do not need to be a workspace admin to set a refresh schedule. You only need the correct permissions on the object.
NEW QUESTION # 28
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?
- A. All files with the .customers extension are deleted.
- B. The accounts.customers table is removed from the metastore, but the underlying data files are untouched.
- C. Running SELECT * FROM accounts.customers will return all rows in the table.
- D. Running SELECT * FROM delta. `dbfs:/stakeholders/customers` results in an error.
- E. The accounts.customers table is removed from the metastore, and the underlying data files are deleted.
Answer: B
Explanation:
the accounts.customers table is an EXTERNAL table, which means that it is stored outside the default warehouse directory and is not managed by Databricks. Therefore, when you run the DROP command on this table, it only removes the metadata information from the metastore, but does not delete the actual data files from the file system. This means that you can still access the data using the location path (dbfs:/stakeholders/customers) or create another table pointing to the same location. However, if you try to query the table using its name (accounts.customers), you will get an error because the table no longer exists in the metastore. Reference: DROP TABLE | Databricks on AWS, Best practices for dropping a managed Delta Lake table - Databricks
NEW QUESTION # 29
A data analyst runs the following command:
SELECT age, country
FROM my_table
WHERE age >= 75 AND country = 'canada';
Which of the following tables represents the output of the above command?
- A.
- B.
- C.
- D.
- E.
Answer: E
Explanation:
The SQL query provided is designed to filter out records from "my_table" where the age is 75 or above and the country is Canada. Since I can't view the content of the links provided directly, I need to rely on the image attached to this question for context. Based on that, Option E (the image attached) represents a table with columns "age" and "country", showing records where age is 75 or above and country is Canada. Reference: The answer can be inferred from understanding SQL queries and their outputs as per Databricks documentation: Databricks SQL
NEW QUESTION # 30
In which circumstance will there be a substantial difference between the variable's mean and median values?
- A. When the variable is of the boolean type
- B. When the variable is of the categorical type
- C. When the variable contains no outliers
- D. When the variable contains a lot of extreme outliers
Answer: D
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 # 31
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?
- A. The query is selecting region but region should only occur in the ORDER BY clause.
- B. There are no mistakes in the query.
- C. The query is using ORDER BY. which is not allowed in an aggregation.
- D. The query is using count('). which will count all the customers in the customers table, no matter the region.
- E. The query is missing a GROUP BY region clause.
Answer: E
Explanation:
In the provided SQL query, the data analyst is trying to count the number of customers in each region. However, they made a mistake by not including the "GROUP BY" clause to group the results by region. Without this clause, the query will not return counts for each distinct region but rather an error or incorrect result. Reference: The need for a GROUP BY clause in such queries can be understood from Databricks SQL documentation: Databricks SQL.
I also noticed that you uploaded an image with your question. The image shows a snippet of an SQL query written in plain text on a white background. The query is attempting to select regions and count customers from a "customers" table and order the results by region. There's no visible syntax highlighting or any other color - it's monochromatic. The query is the same as the one in your question. I'm not sure why you included the image, but maybe you wanted to show me the exact format of your query. If so, you can also use code blocks to display formatted content such as SQL queries. For example, you can write:
SELECT region, count(*) AS number_of_customers
FROM customers
ORDER BY region;
This way, you can avoid uploading images and make your questions more clear and concise. I hope this helps.
NEW QUESTION # 32
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 create two separate dashboards.
- B. They will need to add two separate visualizations to the dashboard based on the same Query.
- C. They will need to decide on a single data visualization to add to the dashboard.
- D. They will need to alter the Query to return two separate sets of results.
- E. They will need to copy the Query and create one data visualization per query.
Answer: B
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 # 33
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?
- A. Edit the Owner field in the table page by selecting the Admins group
- B. Edit the Owner field in the table page by removing all access
- C. Edit the Owner field in the table page by selecting All Users
- D. Edit the Owner field in the table page by selecting the new owner's account
- E. Edit the Owner field in the table page by removing their own account
Answer: D
Explanation:
The Owner field in the table page shows the current owner of the table and allows the owner to change it to another user or group. To change the ownership of the table, the owner can click on the Owner field and select the new owner from the drop-down list. This will transfer the ownership of the table to the selected user or group and remove the previous owner from the list of table access control entries1. The other options are incorrect because:
A . Removing the owner's account from the Owner field will not change the ownership of the table, but will make the table ownerless2.
B . Selecting All Users from the Owner field will not change the ownership of the table, but will grant all users access to the table3.
D . Selecting the Admins group from the Owner field will not change the ownership of the table, but will grant the Admins group access to the table3.
E . Removing all access from the Owner field will not change the ownership of the table, but will revoke all access to the table4. Reference:
1: Change table ownership
2: Ownerless tables
3: Table access control
4: Revoke access to a table
NEW QUESTION # 34
Which of the following is an advantage of using a Delta Lake-based data lakehouse over common data lake solutions?
- A. Data deletion
- B. Scalable storage
- C. ACID transactions
- D. Flexible schemas
- E. Open-source formats
Answer: C
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 # 35
What is used as a compute resource for Databricks SQL?
- A. SQL warehouses
- B. Single-node clusters
- C. Standard clusters
- D. Downstream BI tools integrated with Databricks SQL
Answer: A
NEW QUESTION # 36
Data professionals with varying titles use the Databricks SQL service as the primary touchpoint with the Databricks Lakehouse Platform. However, some users will use other services like Databricks Machine Learning or Databricks Data Science and Engineering.
Which of the following roles uses Databricks SQL as a secondary service while primarily using one of the other services?
- A. Business analyst
- B. SQL analyst
- C. Business intelligence analyst
- D. Data analyst
- E. Data engineer
Answer: E
Explanation:
Data engineers are primarily responsible for building, managing, and optimizing data pipelines and architectures. They use Databricks Data Science and Engineering service to perform tasks such as data ingestion, transformation, quality, and governance. Data engineers may use Databricks SQL as a secondary service to query, analyze, and visualize data from the lakehouse, but this is not their main focus. Reference: Databricks SQL overview, Databricks Data Science and Engineering overview, Data engineering with Databricks
NEW QUESTION # 37
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 convert to a Dashboard Parameter.
- C. 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.
- D. The area chart will use whatever value is chosen on the dashboard at the time the area chart is added to the dashboard.
- E. 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.
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 # 38
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?
- A. Queries that return results based on dates cannot be used with Alerts.
- B. Queries that use query parameters cannot be used with Alerts.
- C. Alerts don't work with queries that access tables.
- D. The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.
- E. The wrong query parameter is being used. Alerts only work with Date and Time query parameters.
Answer: B
Explanation:
The reason the alert is not functioning as expected is because Databricks SQL Alerts do not support query parameters. This limitation applies to all types of parameters, including date parameters.
Here's why:
Alerts require static, deterministic query results so they can compare values consistently during scheduled executions.
When a query includes parameters (e.g., a date range parameter), its results may change based on user input or the default value set in the query editor.
However, Databricks SQL Alerts will always use the default value set for the parameter at the time the alert is created. This means the alert doesn't dynamically adapt to new date ranges and will not reflect changes unless the query is manually updated.
As a result, if the business logic behind the alert depends on changing date ranges or any user input, the alert will not trigger correctly, or may never trigger at all.
Therefore, the correct explanation contradicts Option B, which is incorrect in saying that alerts cannot work with date-based queries at all. In fact, they can-as long as the query is static (i.e., without parameters).
Reference:
Databricks SQL Alerts Documentation
Databricks Knowledge: "You cannot use alerts with queries that contain parameters."
NEW QUESTION # 39
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 create two separate dashboards.
- B. They will need to add two separate visualizations to the dashboard based on the same Query.
- C. They will need to decide on a single data visualization to add to the dashboard.
- D. They will need to alter the Query to return two separate sets of results.
- E. They will need to copy the Query and create one data visualization per query.
Answer: B
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 # 40
A data scientist has asked a data analyst to create histograms for every continuous variable in a data set. The data analyst needs to identify which columns are continuous in the data set.
What describes a continuous variable?
- A. A quantitative variable that can take on an uncountable set of values
- B. A quantitative variable that never stops changing
- C. A categorical variable in which the number of categories continues to increase over time
- D. A quantitative variable Chat can take on a finite or countably infinite set of values
Answer: A
Explanation:
A continuous variable is a type of quantitative variable that can assume an infinite number of values within a given range. This means that between any two possible values, there can be an infinite number of other values. For example, variables such as height, weight, and temperature are continuous because they can be measured to any level of precision, and there are no gaps between possible values. This is in contrast to discrete variables, which can only take on specific, distinct values (e.g., the number of children in a family). Understanding the nature of continuous variables is crucial for data analysts, especially when selecting appropriate statistical methods and visualizations, such as histograms, to accurately represent and analyze the data.
NEW QUESTION # 41
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