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

Section	Weight	Objectives
Topic 1: Data Modeling with Databricks SQL	5%	- Delta table structure - Schema design principles - Performance-oriented modeling
Topic 2: Creating Dashboards and Visualizations in Databricks	16%	- Dashboard creation and layout - Filtering and interactivity - Visualization types and best practices - Scheduling and sharing dashboards
Topic 3: Securing Data	8%	- Data governance policies - Secure storage and compliance - Access control and permissions
Topic 4: Importing Data	5%	- UI-based data ingestion - Databricks Marketplace - API and Auto Loader - Delta Sharing - S3 and cloud storage integration
Topic 5: Executing Queries using Databricks SQL and Databricks SQL Warehouses	20%	- Aggregations and grouping - Creating and managing views - ANSI SQL syntax and functions - Joining and combining datasets - Warehouse configuration and performance
Topic 6: Developing, Sharing, and Maintaining AI/BI Genie Spaces	12%	- Natural language query setup - Genie space setup and configuration - Access control and sharing - Maintenance and improvement
Topic 7: Managing Data	8%	- Data cleaning and preparation - Unity Catalog usage - Discovering and registering datasets - Dataset versioning and management
Topic 8: Understanding of Databricks Data Intelligence Platform	11%	- Lakehouse platform fundamentals - Workspace navigation and interface - Core architecture and components
Topic 9: Analyzing Queries	15%	- Query history and auditing - Liquid clustering and indexing - Performance optimization - Execution plans and analysis

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Databricks Certified Data Analyst Associate Exam 認定 Databricks-Certified-Data-Analyst-Associate 試験問題 (Q42-Q47):

質問 # 42
A data analyst is working with the following table my_table:
customer_name dollars_spent
Hex Sprockets [125.34, 100.15, 9003.99]
Dented Fenders [16.99, 200.85, 33.49, 88.17]
The analyst wants to divide each value in the dollars_spent array by 100 to get the spend in terms of hundreds of dollars using the following code block:
SELECT
customer_name,
_______
FROM my_table;
Which line of code can be used to fill in the blank so that the above code block successfully completes the task?

A. TRANSFORM(dollars_spent, dollars_spent / 100) AS hundreds_spent
B. TRANSFORM(dollars_spent, value - > value / 100) AS hundreds_spent
C. TRANSFORM(hundreds_spent, dollars_spent / 100)
D. TRANSFORM(dollars_spent, value / 100) AS hundreds_spent

正解：B

解説：
The correct answer is C because TRANSFORM applies a lambda function to each element of an array and returns a new array. The expression value - > value / 100 means: for each element named value in dollars_spent, divide that element by 100. The result is then aliased as hundreds_spent. Options A, B, and D do not use the required lambda syntax correctly.
Official documentation extract used: Databricks states that transform returns an array and that the lambda function "produces a new value for each element."

質問 # 43
A data analyst has two data sources that are providing similar but complementary information. The analyst wants to combine these sources of data into a single, comprehensive dataset for ongoing use for their team in a variety of different projects.
Which term is used to describe this type of work?

A. Data blending
B. Data testing
C. Ad-hoc improvements
D. Last-mile ETL

正解：A

解説：
Option D is correct. The scenario describes combining multiple complementary data sources into one comprehensive dataset. That is data blending. Last-mile ETL is usually project-specific final transformation near the end of an analytics workflow, while this question emphasizes combining two source datasets for broader ongoing team use. The current official Databricks exam guide describes the same kind of capability as creating unified datasets by joining data from multiple sources. That aligns with the concept of data blending. Reference: Databricks Certified Data Analyst Associate Exam Guide.

質問 # 44
A data analyst runs the following command:
INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers;
What is the result of running this command?

A. The command fails because it is written incorrectly.
B. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, and any duplicate data is deleted.
C. The suppliers table now contains only the data from the new suppliers table.
D. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, including any duplicate data.
E. The suppliers table now contains the data from the new suppliers table, and the new suppliers table now contains the data from the suppliers table.

正解：A

解説：
The command INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers is not a valid syntax for inserting data into a table in Databricks SQL. According to the documentation12, the correct syntax for inserting data into a table is either:
* INSERT { OVERWRITE | INTO } [ TABLE ] table_name [ PARTITION clause ] [ ( column_name [,
...] ) | BY NAME ] query
* INSERT INTO [ TABLE ] table_name REPLACE WHERE predicate query
The command in the question is missing the OVERWRITE or INTO keyword, and the query part that specifies the source of the data to be inserted. The TABLE keyword is optional and can be omitted.
The PARTITION clause and the column list are also optional and depend on the table schema and the data source. Therefore, the command in the question will fail with a syntax error.
INSERT | Databricks on AWS
INSERT - Azure Databricks - Databricks SQL | Microsoft Learn

質問 # 45
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 convert to a Dashboard Parameter.
B. The area chart will use whatever value is chosen on the dashboard at the time the area chart is added to the dashboard.
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 input by the analyst when the visualization is added to the dashboard. The parameter cannot be changed by the user afterwards.
E. 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.

正解：E

解説：
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. References: Databricks SQL dashboards, Query parameters

質問 # 46
A data analyst is working with a nested array column products in table transactions. The analyst wants to return the first item in the array for each row.
The data analyst is using the following incomplete command:
SELECT
transaction_id,
_____ AS first_product
FROM transactions;
Which line of code should the data analyst use to fill in the blank so that it successfully completes the task?

A. products.1
B. products.0
C. products[0]
D. products[1]

正解：C

解説：
The correct answer is C because Databricks SQL supports bracket notation for arrays, and array indexing starts at 0. Therefore, the first element of the products array is accessed with products[0]. Options A and B use dot notation, which is not the correct syntax for array indexing. Option D returns the second element, not the first.
Official documentation extract used: Databricks states that the bracket operator returns an array element by index and that "the first element of an ARRAY is at index 0."

質問 # 47
......

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素晴らしいDatabricks-Certified-Data-Analyst-Associate日本語講座一回合格-高品質なDatabricks-Certified-Data-Analyst-Associate復習資料

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

最新のDatabricks-Certified-Data-Analyst-Associate日本語講座 & 合格スムーズDatabricks-Certified-Data-Analyst-Associate復習資料 | 有効的なDatabricks-Certified-Data-Analyst-Associate認定資格試験問題集 Databricks Certified Data Analyst Associate Exam

Databricks Certified Data Analyst Associate Exam 認定 Databricks-Certified-Data-Analyst-Associate 試験問題 (Q42-Q47):