Databricks Databricks-Certified-Data-Analyst-Associate試験問題集シミュレーターパス率100％問題集

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

SectionWeightObjectives
Topic 1: Analyzing Queries15%- Query Analysis
  • 1. Reading Query Plans
  • 2. Performance Tuning
  • 3. Monitoring Query Execution
Topic 2: Importing Data5%- Data Import
  • 1. Loading CSV and JSON Data
  • 2. Uploading Files
  • 3. Creating Tables from Imported Data
Topic 3: Creating Dashboards and Visualizations16%- Visualization
  • 1. Dashboards
  • 2. Alerts and Filters
  • 3. Sharing and Publishing
  • 4. Charts and Graphs
Topic 4: Data Modeling with Databricks SQL5%- Modeling Concepts
  • 1. Views and Relationships
  • 2. Fact and Dimension Tables
  • 3. Data Organization
Topic 5: Developing AI/BI Genie Spaces12%- AI and BI Features
  • 1. Workspace Configuration
  • 2. AI/BI Genie
  • 3. Natural Language Queries
Topic 6: Managing Data8%- Data Management
  • 1. Cleaning Data with SQL
  • 2. Discovering Data Assets
  • 3. Handling Missing and Invalid Data
  • 4. Tagging and Lineage
Topic 7: Securing Data8%- Security and Access Control
  • 1. Data Access Policies
  • 2. Unity Catalog Security
  • 3. Permissions and Roles
Topic 8: Executing Queries Using Databricks SQL and SQL Warehouses20%- SQL Querying
  • 1. Joins and Subqueries
  • 2. SQL Warehouses
  • 3. Filtering and Aggregation
  • 4. Writing SQL Queries
  • 5. Query Optimization
Topic 9: Understanding of Databricks Data Intelligence Platform11%- Platform Components
  • 1. Databricks SQL
  • 2. Lakeflow Jobs
  • 3. Delta Lake
  • 4. Unity Catalog
  • 5. Mosaic AI
- Catalog and Governance
  • 1. Catalogs and Schemas
  • 2. Data Lineage
  • 3. Managed and External Tables
  • 4. Views and Certified Tables

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Databricks Certified Data Analyst Associate Exam 認定 Databricks-Certified-Data-Analyst-Associate 試験問題 (Q30-Q35):

質問 # 30
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?

正解：C

解説：
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


質問 # 31
A data analyst wants to generate insights from large, complex datasets. The analyst needs to quickly understand the meaning of various data columns, ask questions in natural language, and receive AI-driven recommendations for optimizing data queries and workflows.
Which Databricks component is primarily responsible for enabling these capabilities?

正解：D

解説：
Option A is correct. The Data Intelligence Engine is the platform-level intelligence layer that understands the semantics and uniqueness of an organization's data and enables AI-assisted experiences across Databricks.
Unity Catalog provides governance and metadata management, Genie Spaces provide a natural-language interface for curated business data, and Databricks Assistant is a user-facing assistant for code/query help.
However, the question asks which component is primarily responsible for enabling these capabilities across the platform; that is the Data Intelligence Engine. Databricks describes the platform as powered by a Data Intelligence Engine that understands the uniqueness and semantics of data and helps optimize performance.
References: Databricks Data Intelligence Platform documentation and Data Analyst Associate Exam Guide.


質問 # 32
In which of the following situations should a data analyst use higher-order functions?

正解：B

解説：
Higher-order functions are a simple extension to SQL to manipulate nested data such as arrays. A higher-order function takes an array, implements how the array is processed, and what the result of the computation will be. It delegates to a lambda function how to process each item in the array. This allows you to define functions that manipulate arrays in SQL, without having to unpack and repack them, use UDFs, or rely on limited built-in functions. Higher-order functions provide a performance benefit over user defined functions. Reference: Higher-order functions | Databricks on AWS, Working with Nested Data Using Higher Order Functions in SQL on Databricks | Databricks Blog, Higher-order functions - Azure Databricks | Microsoft Learn, Optimization recommendations on Databricks | Databricks on AWS


質問 # 33
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?

正解：E

解説：
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. References:
The answer can be inferred from understanding SQL queries and their outputs as per Databricks documentation: Databricks SQL


質問 # 34
What describes the variance of a set of values?

正解：C

解説：
Variance is a statistical measure that quantifies the dispersion or spread of a set of values around their mean (central value). It is calculated by taking the average of the squared differences between each value and the mean of the dataset. A higher variance indicates that the data points are more spread out from the mean, while a lower variance suggests that they are closer to the mean. This measure is fundamental in statistics to understand the degree of variability within a dataset.WikipediaWikipedia+1Investopedia+1


質問 # 35
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