Databricks-Certified-Data-Analyst-Associate認定資格、Databricks-Certified-Data-Analyst-Associate模擬体験

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

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

>> Databricks-Certified-Data-Analyst-Associate認定資格 <<

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Databricks Certified Data Analyst Associate Exam 認定 Databricks-Certified-Data-Analyst-Associate 試験問題 (Q51-Q56):

質問 # 51
Where in the Databricks SQL workspace can a data analyst configure a refresh schedule for a query when the query is not attached to a dashboard or alert?

正解：A

解説：
In Databricks SQL, to configure a refresh schedule for a query that is not attached to a dashboard or alert, a data analyst should use the Query Editor. Within the Query Editor, there is an option to set up scheduled executions for queries. This feature enables the query to run at specified intervals, ensuring that the results are updated regularly. By scheduling queries in this manner, analysts can automate data refreshes and maintain up-to-date query results without manual intervention.


質問 # 52
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?

正解：B

解説：
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. References: Databricks - Last-mile ETL, Databricks - Data Analysis with Databricks SQL


質問 # 53
A data analyst runs the following command:
INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers;
What is the result of running this command?

正解：E

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 # 54
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?

正解：A

解説：
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


質問 # 55
In which of the following situations should a data analyst use higher-order functions?

正解：A

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 # 56
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