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

SectionWeightObjectives
Analyzing Queries15%- Query Analysis
  • 1. Performance Tuning
  • 2. Reading Query Plans
  • 3. Monitoring Query Execution
Managing Data8%- Data Management
  • 1. Tagging and Lineage
  • 2. Handling Missing and Invalid Data
  • 3. Discovering Data Assets
  • 4. Cleaning Data with SQL
Securing Data8%- Security and Access Control
  • 1. Unity Catalog Security
  • 2. Data Access Policies
  • 3. Permissions and Roles
Creating Dashboards and Visualizations16%- Visualization
  • 1. Sharing and Publishing
  • 2. Alerts and Filters
  • 3. Charts and Graphs
  • 4. Dashboards
Understanding of Databricks Data Intelligence Platform11%- Catalog and Governance
  • 1. Views and Certified Tables
  • 2. Managed and External Tables
  • 3. Catalogs and Schemas
  • 4. Data Lineage
- Platform Components
  • 1. Delta Lake
  • 2. Mosaic AI
  • 3. Lakeflow Jobs
  • 4. Databricks SQL
  • 5. Unity Catalog
Importing Data5%- Data Import
  • 1. Loading CSV and JSON Data
  • 2. Creating Tables from Imported Data
  • 3. Uploading Files
Developing AI/BI Genie Spaces12%- AI and BI Features
  • 1. Workspace Configuration
  • 2. Natural Language Queries
  • 3. AI/BI Genie
Executing Queries Using Databricks SQL and SQL Warehouses20%- SQL Querying
  • 1. Writing SQL Queries
  • 2. Joins and Subqueries
  • 3. Query Optimization
  • 4. SQL Warehouses
  • 5. Filtering and Aggregation
Data Modeling with Databricks SQL5%- Modeling Concepts
  • 1. Data Organization
  • 2. Fact and Dimension Tables
  • 3. Views and Relationships

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Databricks Certified Data Analyst Associate Exam Databricks-Certified-Data-Analyst-Associate Prüfungsfragen mit Lösungen (Q24-Q29):

24. Frage
A database was created in Databricks SQL using the following statement:
CREATE SCHEMA accounting LOCATION ' dbfs:/accounting/data ' ;
Where will data for this database be stored?

Antwort: B

Begründung:
Option B is correct. The statement explicitly sets the schema/database location to dbfs:/accounting/data. In Databricks SQL, CREATE SCHEMA supports a LOCATION schema_directory, and the documentation explains that the schema directory is the filesystem path where the schema is created. Because the user supplied LOCATION ' dbfs:/accounting/data ' , Databricks uses that path instead of the default warehouse directory. Option D would apply only when no custom location is supplied. References: Databricks CREATE SCHEMA documentation.


25. Frage
What does Partner Connect do when connecting Power Bl and Tableau?

Antwort: C

Begründung:
When connecting Power BI and Tableau through Databricks Partner Connect, the system automates several steps to streamline the integration process:
* Personal Access Token Creation: Partner Connect generates a Databricks personal access token, which is essential for authenticating and establishing a secure connection between Databricks and the BI tools.
* ODBC Driver Installation: The appropriate ODBC driver is downloaded and installed. This driver facilitates communication between the BI tools and Databricks, ensuring compatibility and optimal performance.
* Configuration File Download: A configuration file tailored for the selected BI tool (Power BI or Tableau) is provided. This file contains the necessary connection details, simplifying the setup process within the BI tool.
By automating these steps, Partner Connect ensures a seamless and efficient integration, reducing manual configuration efforts and potential errors.
Reference: Connect Tableau and Databricks


26. Frage
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)

D)

Antwort: D

Begründung:
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(). References:
* 1: PERCENT_RANK (Transact-SQL)
* 2: Window functions in Databricks SQL
* 3: Databricks Certified Data Analyst Associate Exam Guide


27. Frage
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?

Antwort: A

Begründung:
Databricks SQL is not meant to replace or substitute other BI tools, but rather to complement them by providing a fast and easy way to query, explore, and visualize data on the lakehouse using the built-in SQL editor, visualizations, and dashboards. Databricks SQL also integrates seamlessly with popular BI tools like Tableau, Power BI, and Looker, allowing analysts to use their preferred tools to access data through Databricks clusters and SQL warehouses. Databricks SQL offers low-code and no-code experiences, as well as optimized connectors and serverless compute, to enhance the productivity and performance of BI workloads on the lakehouse. References: Databricks SQL, Connecting Applications and BI Tools to Databricks SQL, Databricks integrations overview, Databricks SQL: Delivering a Production SQL Development Experience on the Lakehouse


28. Frage
A data analyst is working on a DataFrame named dates_df and needs to add a new column, date, derived from the timestamp field.
Which code fragment should be used to extract the date from a timestamp?

Antwort: A

Begründung:
Option B is correct. The function to_date converts a timestamp or date-like expression to a date value, which matches the requirement to extract the date from the timestamp field. unix_timestamp converts to a Unix timestamp value, date_format formats a date or timestamp as a string, and from_unixtime converts Unix time into a timestamp/string representation. Official Databricks documentation states that to_date(expr [, fmt]) returns the expression cast to a date.


29. Frage
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