Databricks Data Analyst Associate Exam Dumps 2024
Databricks Data Analyst Associate Exam Dumps 2024
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Question 1:
Which of the following is a critical organization-specific consideration when handling PII data?
A. Implementing a one-size-fits-all approach to PII data storage and processing.
B. Developing a uniform public access policy for all PII data.
C. Prioritizing cost-saving measures over data security for PII data.
D. Adapting PII data handling protocols to comply with regional and sector-specific privacy laws.
E. Always using the same encryption method for PII data across all departments.
Answer: D
Explanation:
When managing PII data, it is essential for organizations to consider the legal and regulatory environment they operate in.
This often requires adapting data handling protocols to comply with various regional and sector-specific privacy laws.
Unlike options A, B, C, and E, which suggest uniform or generalized approaches, adapting to specific legal requirements ensures both compliance and the security of sensitive data.
This adaptation may involve different encryption methods, storage solutions, and access policies depending on the jurisdiction and industry of the organization.
References:
https://www.digitalguardian.com/blog/pii-data-classification-4-best-practices
Question 2:
What are the essential steps to execute a basic SQL query in Databricks?
A. Write a SQL query in a Databricks notebook, validate the syntax, execute the query, and view the results.
B. Manually enter data into Databricks tables, write a SQL query in a text file, and use an external tool to execute the query.
C. Open SQL Editor, select a SQL warehouse, specify the query, run the query.
D. Create a data frame in Python or Scala, apply a SQL query to the data frame, and display the results.
E. Import data into a Databricks dataset, use a BI tool to run the SQL query, and export the results to a CSV file.
Answer: C
Explanation:
To execute a basic SQL query in Databricks, the typical process involves using the Databricks SQL Editor interface.
Here, you write the SQL query, select the appropriate data source or table you want to query against, run the query, and then view or visualize the results directly within the interface.
This process allows for a seamless experience in querying and analyzing data using SQL within the Databricks environment.
References:
https://docs.databricks.com/en/sql/user/queries/queries.html
https://docs.databricks.com/en/sql/get-started/index.html
Question 3:
What are the primary responsibilities of a table owner in Databricks?
A. Designing the visual representation of the table data.
B. Ensuring the table is always available for querying and analysis.
C. Regularly updating the table data to keep it current.
D. Optimizing the table for faster query performance.
E. Managing user access and permissions for the table.
Answer: E
Explanation:
The table owner in Databricks is primarily responsible for managing user access and permissions related to the table.
This involves setting and adjusting who can view, modify, or delete the table, ensuring proper data governance and security.
The owner‘s role is crucial in maintaining the integrity and confidentiality of the data contained within the table.
References:
https://docs.databricks.com/en/data-governance/unity-catalog/manage-privileges/ownership.html
Question 4:
In Databricks SQL, when creating a basic, schema-specific visualization, what is the first step you should take?
A. Select the visualization type from the visualization menu.
B. Configure the dashboard settings to match the schema requirements.
C. Write a SQL query to retrieve data from the specific schema.
D. Import external visualization libraries for advanced charting.
E. Adjust the data refresh rate to ensure real-time visualization.
Answer: C
Explanation:
When creating basic, schema-specific visualizations using Databricks SQL, the first step is to write a SQL query to retrieve the data you want to visualize.
This query will specify the schema you are working with and select the relevant data for visualization.
Once you have retrieved the data, you can then proceed to choose the visualization type and configure the visualization settings based on your schema-specific requirements.
References:
https://learn.microsoft.com/en-us/azure/databricks/sql/get-started/visualize-data-tutorial
Question 5:
In a Databricks SQL context, consider a dataset with columns ‘Department‘, ‘Employee‘, and ‘Sales‘. You are required to analyze the data using the ROLLUP and CUBE functions.
Given this scenario, select the correct statement regarding the type of aggregations ROLLUP and CUBE would generate when applied to the ‘Department‘and ‘Employee‘ columns.
A. ROLLUP generates hierarchical aggregations starting from the leftmost column in the GROUP BY clause. It would produce subtotals for each ‘Department‘, subtotals for each combination of ‘Department‘ and ‘Employee‘, and a grand total.
B. Neither ROLLUP nor CUBE will generate subtotals for individual ‘Departments‘ or ‘Employees‘; they only provide a grand total.
C. Both ROLLUP and CUBE produce identical aggregations, including subtotals for each ‘Department‘, each ‘Employee‘, each combination of ‘Department‘ and ‘Employee‘, and a grand total.
D. ROLLUP provides aggregations only for each combination of ‘Department‘ and ‘Employee‘, while CUBE gives a detailed breakdown including each ‘Department‘, each ‘Employee‘, and a grand total.
E. CUBE creates aggregations for all possible combinations of the columns in the GROUP BY clause. It would generate subtotals for each ‘Department‘, each ‘Employee‘, each combination of ‘Department‘ and ‘Employee‘, and a grand total.
Answer: A
Explanation:
ROLLUP is used for hierarchical data aggregation.
It starts with the most detailed level (in this case, ‘Department‘ and ‘Employee‘) and rolls up to broader levels (‘Department‘), ending with a grand total.
The other options are not correct as they either misinterpret the functions of ROLLUP and CUBE or provide an incomplete or inaccurate description of the aggregations these functions generate.
Understanding the functionality of ROLLUP and CUBE is essential for SQL data analysis, particularly in platforms like Databricks, where complex data manipulation and aggregation are common tasks.
References:
https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-qry-select-groupby
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Question 6:
In Azure Databricks, when using a table visualization type for SQL queries, which of the following statements accurately reflects its capabilities and limitations?
A. They allow for manual reordering, hiding, and formatting of data columns, but do not perform data aggregations within the result set.
B. Table visualizations primarily function to display graphical representations like charts and graphs, rather than tabular data.
C. They are limited to displaying only numerical data and cannot handle textual or categorical data.
D. Table visualizations in Databricks are primarily used for external data export and are not suitable for in-dashboard data presentation.
E. Table visualizations in Databricks automatically aggregate data within the result set, providing a summary view.
Answer: A
Explanation:
Table visualizations in Azure Databricks provide a flexible way to present data in a structured table format.
Users can manually reorder, hide, and format the data columns to suit their analysis needs.
However, it‘s important to note that these visualizations do not perform any data aggregations within the result set.
All necessary aggregations must be computed within the SQL query before visualization.
References:
https://learn.microsoft.com/en-us/azure/databricks/visualizations/visualization-types#table
Question 7:
In the context of statistics, what are key moments of a statistical distribution?
A. The range, interquartile range, and standard deviation, which describe the variability of the distribution.
B. The mean, variance, skewness, and kurtosis, which are the first four moments of a distribution.
C. The maximum and minimum values, which set the boundaries of the distribution.
D. The skewness and kurtosis, which describe the shape and tail behavior of the distribution.
E. The mean, median, and mode, which define the central tendency of the distribution.
Answer: B
Explanation:
Key moments of a statistical distribution include the mean (first moment), variance (second moment), skewness (third moment), and kurtosis (fourth moment).
These moments are crucial in describing the characteristics of a distribution. The mean measures the central tendency, variance measures the dispersion, skewness indicates the asymmetry, and kurtosis describes the ‘tailedness‘ of the distribution.
Understanding these moments is essential for comprehensively describing and analyzing the behavior of data in a statistical context.
References:
https://www.analyticsvidhya.com/blog/2022/01/moments-a-must-known-statistical-concept-for-data-science/
Question 8:
What are the primary benefits of implementing Delta Lake within the Databricks Lakehouse architecture?
A. Delta Lake primarily enhances data security and compliance features.
B. Delta Lake provides ACID transactions, scalable metadata handling, and time-travel features.
C. It offers high-speed streaming data ingestion and real-time analytics capabilities.
D. Delta Lake is beneficial only for handling unstructured data types.
E. It mainly improves the graphical user interface for data exploration.
Answer: B
Explanation:
Delta Lake brings significant benefits to the Lakehouse architecture in Databricks by offering ACID transactions to ensure data integrity, scalable metadata handling for large datasets, and time-travel capabilities allowing users to access historical data.
These features enable more robust data management, improved data reliability, and enhanced analytical capabilities, making Delta Lake a powerful component of the Lakehouse architecture.
References:
https://www.databricks.com/product/delta-lake-on-databricks
Question 9:
data analyst is working with a database in Databricks and needs to update a table named SalesData.
The analyst has a new batch of data that includes some records already present in SalesData and some new records. They must decide between using MERGE INTO, INSERT INTO, and COPY INTO commands.
Considering the functions and typical use cases of these commands, which of the following statements is true?
A. INSERT INTO can be used for both updating existing records and inserting new records, while MERGE INTO is only for inserting new records, and COPY INTO is not used in Databricks.
B. MERGE INTO and INSERT INTO perform the same functions, and COPY INTO is not a recognized command in Databricks.
C. MERGE INTO is suitable for updating existing records and inserting new records, while INSERT INTO is used only for adding new records, and COPY INTO is used for loading data from files.
D. INSERT INTO and COPY INTO are both used for inserting new records, but COPY INTO is specifically for loading data from external sources, and MERGE INTO is for updating existing records only.
E. COPY INTO is used for updating existing records and inserting new records, MERGE INTO is only for inserting new records, and INSERT INTO is not used in Databricks.
Answer: C
Explanation:
MERGE INTO is suitable for both updating existing records and inserting new records depending on whether a match is found in the target table.
INSERT INTO is typically used to add new records to a table and does not update existing records.
COPY INTO is a specialized command used in Databricks for loading data into a table from external sources such as files in a file system.
MERGE INTO is used for complex operations where you need to update existing records or insert new ones based on some matching condition.
INSERT INTO adds new rows to a table and is straightforward for adding new data.
COPY INTO is specifically designed for loading data from files into a table, useful when importing data from external sources.
References:
https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-merge-into
https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-dml-insert-into
https://learn.microsoft.com/en-us/azure/databricks/ingestion/copy-into/
Question 10:
In Databricks, a data analyst is working on a dashboard composed of multiple visualizations and wants to ensure a consistent color scheme across all visualizations for better aesthetic coherence and readability. Which approach should the analyst take to change the colors of all the visualizations in the dashboard?
A. Use a dashboard-wide setting that allows the analyst to apply a uniform color scheme to all visualizations simultaneously.
B. Change the default color settings in the Databricks user preferences to automatically apply to all dashboards and visualizations.
C. Export the dashboard data to a third-party tool for color scheme adjustments, then re-import it into Databricks.
D. Manually adjust the color settings in each individual visualization to match the desired scheme.
E. Implement a script in the dashboard code to automatically adjust the colors of all visualizations.
Answer: A
Explanation:
For efficiency and consistency, the ideal approach in Databricks is to utilize a dashboard-wide setting that enables the application of a consistent color scheme across all visualizations.
This method is more effective and time-saving compared to manually adjusting each visualization, and it ensures uniformity in the presentation of the dashboard.
Such a feature allows for easy updates and modifications to the color scheme without the need for individual adjustments or external tools.
References:
https://docs.databricks.com/en/sql/user/dashboards/index.html#customize-dashboard-colors
For a full set of 270+ questions. Go to
https://skillcertpro.com/product/databricks-data-analyst-associate-exam-questions/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.