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Prepare Databricks-Certified-Data-Analyst-Associate Exam Questions [2024] Recently Updated Questions
Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:
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NEW QUESTION # 15
Which of the following approaches can be used to ingest data directly from cloud-based object storage?
- A. Create an external table while specifying the object storage path to LOCATION
- B. Create an external table while specifying the object storage path to FROM
- C. Create an external table while specifying the DBFS storage path to PATH
- D. Create an external table while specifying the DBFS storage path to FROM
- E. It is not possible to directly ingest data from cloud-based object storage
Answer: A
Explanation:
External tables are tables that are defined in the Databricks metastore using the information stored in a cloud object storage location. External tables do not manage the data, but provide a schema and a table name to query the data. To create an external table, you can use the CREATE EXTERNAL TABLE statement and specify the object storage path to the LOCATION clause. For example, to create an external table named ext_table on a Parquet file stored in S3, you can use the following statement:
SQL
CREATE EXTERNAL TABLE ext_table (
col1 INT,
col2 STRING
)
STORED AS PARQUET
LOCATION 's3://bucket/path/file.parquet'
AI-generated code. Review and use carefully. More info on FAQ.
NEW QUESTION # 16
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)
- A. Option C
- B. Option B
- C. Option D
- D. Option A
Answer: B
Explanation:
The correct query to get the percentage rank of products within region by the sales is option B. This query uses the PERCENT_RANK() window function to calculate the relative rank of each product within each region based on the sales amount. The window function is partitioned by region and ordered by sales in descending order. The result is aliased as rank and displayed along with the region and product columns. The other options are incorrect because:
A) Option A uses the RANK() window function instead of the PERCENT_RANK() function. The RANK() function returns the rank of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM().
C) Option C uses the DENSE_RANK() window function instead of the PERCENT_RANK() function. The DENSE_RANK() function returns the rank of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM().
D) Option D uses the ROW_NUMBER() window function instead of the PERCENT_RANK() function. The ROW_NUMBER() function returns the sequential number of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM(). Reference:
1: PERCENT_RANK (Transact-SQL)
2: Window functions in Databricks SQL
3: Databricks Certified Data Analyst Associate Exam Guide
NEW QUESTION # 17
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 using ORDER BY. which is not allowed in an aggregation.
- B. The query is selecting region but region should only occur in the ORDER BY clause.
- C. There are no mistakes in the query.
- 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 # 18
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?
- A. DELETE TABLE table_name FROM database_name;
- B. DELETE TABLE database_name.table_name;
- C. DROP TABLE database_name.table_name;
- D. DROP TABLE table_name FROM database_name;
- E. DROP DATABASE database_name;
Answer: C
Explanation:
The DROP TABLE command removes a table from the metastore and deletes the associated data files. The syntax for this command is DROP TABLE [IF EXISTS] [database_name.]table_name;. The optional IF EXISTS clause prevents an error if the table does not exist. The optional database_name. prefix specifies the database where the table resides. If not specified, the current database is used. Therefore, the correct command to remove the table table_name from the database database_name and all of the data files associated with it is DROP TABLE database_name.table_name;. The other commands are either invalid syntax or would produce undesired results. Reference: Databricks - DROP TABLE
NEW QUESTION # 19
Which of the following statements about a refresh schedule is incorrect?
- A. Refresh schedules can be configured in the Query Editor.
- B. A query being refreshed on a schedule does not use a SQL Warehouse (formerly known as SQL Endpoint).
- C. A query can be refreshed anywhere from 1 minute lo 2 weeks
- D. A refresh schedule is not the same as an alert.
- E. You must have workspace administrator privileges to configure a refresh schedule
Answer: B
Explanation:
Refresh schedules are used to rerun queries at specified intervals, and these queries typically require computational resources to execute. In the context of a cloud data service like Databricks, this would typically involve the use of a SQL Warehouse (or a SQL Endpoint, as they were formerly known) to provide the necessary computational resources. Therefore, the statement is incorrect because scheduled query refreshes would indeed use a SQL Warehouse/Endpoint to execute the query.
NEW QUESTION # 20
A data analyst wants to create a dashboard with three main sections: Development, Testing, and Production. They want all three sections on the same dashboard, but they want to clearly designate the sections using text on the dashboard.
Which of the following tools can the data analyst use to designate the Development, Testing, and Production sections using text?
- A. Separate queries for each section
- B. Direct text written into the dashboard in editing mode
- C. Separate endpoints for each section
- D. Markdown-based text boxes
- E. Separate color palettes for each section
Answer: D
Explanation:
Markdown-based text boxes are useful as labels on a dashboard. They allow the data analyst to add text to a dashboard using the %md magic command in a notebook cell and then select the dashboard icon in the cell actions menu. The text can be formatted using markdown syntax and can include headings, lists, links, images, and more. The text boxes can be resized and moved around on the dashboard using the float layout option. Reference: Dashboards in notebooks, How to add text to a dashboard in Databricks
NEW QUESTION # 21
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. Data analyst
- B. Business analyst
- C. Data engineer
- D. SQL analyst
- E. Business intelligence analyst
Answer: C
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 # 22
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 new owner's account
- 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 removing their own account
- E. Edit the Owner field in the table page by selecting the Admins group
Answer: A
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 # 23
Which of the following statements about adding visual appeal to visualizations in the Visualization Editor is incorrect?
- A. Colors can be changed.
- B. Borders can be added.
- C. Tooltips can be formatted.
- D. Data Labels can be formatted.
- E. Visualization scale can be changed.
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 # 24
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. The wrong query parameter is being used. Alerts only work with Date and Time query parameters.
- B. Alerts don't work with queries that access tables.
- C. Queries that use query parameters cannot be used with Alerts.
- D. Queries that return results based on dates cannot be used with Alerts.
- E. The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.
Answer: C
Explanation:
According to the Databricks documentation1, queries that use query parameters cannot be used with Alerts. This is because Alerts do not support user input or dynamic values. Alerts leverage queries with parameters using the default value specified in the SQL editor for each parameter. Therefore, if the query uses a Date query parameter, the alert will always use the same date range as the default value, regardless of the actual date. This may cause the alert to not work as expected, or to not trigger at all. Reference:
Databricks SQL alerts: This is the official documentation for Databricks SQL alerts, where you can find information about how to create, configure, and monitor alerts, as well as the limitations and best practices for using alerts.
NEW QUESTION # 25
Which of the following approaches can be used to connect Databricks to Fivetran for data ingestion?
- A. Use Partner Connect's automated workflow to establish a cluster for Fivetran to interact with
- B. Use Workflows to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with
- C. Use Partner Connect's automated workflow to establish a SQL warehouse (formerly known as a SQL endpoint) for Fivetran to interact with
- D. Use Delta Live Tables to establish a cluster for Fivetran to interact with
- E. Use Workflows to establish a cluster for Fivetran to interact with
Answer: A
Explanation:
Partner Connect is a feature that allows you to easily connect your Databricks workspace to Fivetran and other ingestion partners using an automated workflow. You can select a SQL warehouse or a cluster as the destination for your data replication, and the connection details are sent to Fivetran. You can then choose from over 200 data sources that Fivetran supports and start ingesting data into Delta Lake. Reference: Connect to Fivetran using Partner Connect, Use Databricks with Fivetran
NEW QUESTION # 26
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?
- A. Data testing
- B. Last-mile
- C. Ad-hoc improvements
- D. Last-mile ETL
- E. Data enhancement
Answer: E
Explanation:
Data enhancement is the process of adding or enriching data with additional information to improve its quality, accuracy, and usefulness. Data enhancement can be used to augment existing data sources with new data sources, such as external datasets, synthetic data, or machine learning models. Data enhancement can help data analysts to gain deeper insights, discover new patterns, and solve complex problems. Data enhancement is one of the applications of generative AI, which can leverage machine learning to generate synthetic data for better models or safer data sharing1.
In the context of the question, the data analyst is working with gold-layer tables, which are curated business-level tables that are typically organized in consumption-ready project-specific databases234. The gold-layer tables are the final layer of data transformations and data quality rules in the medallion lakehouse architecture, which is a data design pattern used to logically organize data in a lakehouse2. The stakeholder has provided the analyst with an additional dataset that can be used to augment the gold-layer tables already in use. This means that the analyst can use the additional dataset to enhance the existing gold-layer tables with more information, such as new features, attributes, or metrics. This data augmentation can help the analyst to complete the ad-hoc project more effectively and efficiently.
Reference:
What is the medallion lakehouse architecture? - Databricks
Data Warehousing Modeling Techniques and Their Implementation on the Databricks Lakehouse Platform | Databricks Blog What is the medallion lakehouse architecture? - Azure Databricks What is a Medallion Architecture? - Databricks Synthetic Data for Better Machine Learning | Databricks Blog
NEW QUESTION # 27
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?
- A. 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.
- B. 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.
- C. Both statements will fail because Databricks SQL does not support those join types.
- D. There is no difference between the result sets for both statements.
- E. 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.
Answer: B
Explanation:
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.
NEW QUESTION # 28
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. Heatmap
- B. IChoropleth
- C. Sankey
- D. Word Cloud
- E. Pivot Table
Answer: C
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 # 29
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 delta. `dbfs:/stakeholders/customers` results in an error.
- D. The accounts.customers table is removed from the metastore, and the underlying data files are deleted.
- E. Running SELECT * FROM accounts.customers will return all rows in the table.
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 # 30
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?
- A. SELECT price FROM customer_summary
- B. SELECT price(customer_spend, customer_units) AS customer_price FROM customer_summary
- C. SELECT function(price(customer_spend, customer_units)) AS customer_price FROM customer_summary
- D. SELECT double(price(customer_spend, customer_units)) AS customer_price FROM customer_summary
- E. SELECT PRICE customer_spend, customer_units AS customer_price FROM customer_summary
Answer: B
Explanation:
A user-defined function (UDF) is a function defined by a user, allowing custom logic to be reused in the user environment1. To apply a UDF to a table, the syntax is SELECT udf_name(column_name) AS alias FROM table_name2. Therefore, option E is the correct way to use the UDF price to create a new column customer_price based on the existing columns customer_spend and customer_units from the table customer_summary. Reference:
What are user-defined functions (UDFs)?
User-defined scalar functions - SQL
V
NEW QUESTION # 31
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 value is chosen on the dashboard at the time the area chart is added to the dashboard.
- B. 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.
- 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 is selected in the Dashboard Parameter along with all of the other visualizations in the dashboard that use the same parameter.
- E. The area chart will convert to a Dashboard Parameter.
Answer: D
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 # 32
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?
- A. Data testing
- B. Last-mile ETL
- C. Data blending
- D. Last-mile
- E. Data enhancement
Answer: B
Explanation:
Last-mile ETL is the term used to describe the additional processing of data that is done by data analysts or data scientists after the data has been ingested, transformed, and stored in the lakehouse by data engineers. Last-mile ETL typically involves tasks such as data cleansing, data enrichment, data aggregation, data filtering, or data sampling that are specific to the analysis or machine learning use case. Last-mile ETL can be done using Databricks SQL, Databricks notebooks, or Databricks Machine Learning. Reference: Databricks - Last-mile ETL, Databricks - Data Analysis with Databricks SQL
NEW QUESTION # 33
A data analyst needs to use the Databricks Lakehouse Platform to quickly create SQL queries and data visualizations. It is a requirement that the compute resources in the platform can be made serverless, and it is expected that data visualizations can be placed within a dashboard.
Which of the following Databricks Lakehouse Platform services/capabilities meets all of these requirements?
- A. Databricks Machine Learning
- B. Delta Lake
- C. Databricks Notebooks
- D. Tableau
- E. Databricks SQL
Answer: E
Explanation:
Databricks SQL is a serverless data warehouse on the Lakehouse that lets you run all of your SQL and BI applications at scale with your tools of choice, all at a fraction of the cost of traditional cloud data warehouses1. Databricks SQL allows you to create SQL queries and data visualizations using the SQL Analytics UI or the Databricks SQL CLI2. You can also place your data visualizations within a dashboard and share it with other users in your organization3. Databricks SQL is powered by Delta Lake, which provides reliability, performance, and governance for your data lake4. Reference:
Databricks SQL
Query data using SQL Analytics
Visualizations in Databricks notebooks
Delta Lake
NEW QUESTION # 34
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