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

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

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Databricks Certified Data Analyst Associate Exam 認定 Databricks-Certified-Data-Analyst-Associate 試験問題 (Q78-Q83):

質問 # 78
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 suppliers table now contains the data from the new suppliers table, and the new suppliers table now contains the data from the suppliers table.
B. 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.
C. The command fails because it is written incorrectly.
D. The suppliers table now contains only the data from the new suppliers table.
E. 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.

正解：C

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.
Reference:
INSERT | Databricks on AWS
INSERT - Azure Databricks - Databricks SQL | Microsoft Learn

質問 # 79
After running DESCRIBE EXTENDED accounts.customers;, the following was returned:

Now, a data analyst runs the following command:
DROP accounts.customers;
Which of the following describes the result of running this command?

A. Running SELECT * FROM delta. `dbfs:/stakeholders/customers` results in an error.
B. All files with the .customers extension are deleted.
C. Running SELECT * FROM accounts.customers will return all rows in the table.
D. The accounts.customers table is removed from the metastore, but the underlying data files are untouched.
E. The accounts.customers table is removed from the metastore, and the underlying data files are deleted.

正解：D

解説：
the accounts.customers table is an EXTERNAL table, which means that it is stored outside the default warehouse directory and is not managed by Databricks. Therefore, when you run the DROP command on this table, it only removes the metadata information from the metastore, but does not delete the actual data files from the file system. This means that you can still access the data using the location path (dbfs:/stakeholders/customers) or create another table pointing to the same location. However, if you try to query the table using its name (accounts.customers), you will get an error because the table no longer exists in the metastore. Reference: DROP TABLE | Databricks on AWS, Best practices for dropping a managed Delta Lake table - Databricks

質問 # 80
What is used as a compute resource for Databricks SQL?

A. Single-node clusters
B. SQL warehouses
C. Downstream BI tools integrated with Databricks SQL
D. Standard clusters

正解：B

解説：
Databricks SQL uses SQL warehouses as its compute resource. A SQL warehouse is a dedicated compute engine designed specifically for executing SQL queries and powering dashboards within the Databricks workspace. According to Databricks official documentation, SQL warehouses are optimized for fast, scalable query execution, whereas clusters are used primarily for data engineering and machine learning workloads.

質問 # 81
Which of the following describes the relationship between Gold tables and Silver tables?

A. Gold tables are more likely to contain valuable data than Silver tables.
B. Gold tables are more likely to contain more data than Silver tables.
C. Gold tables are more likely to contain aggregations than Silver tables.
D. Gold tables are more likely to contain a less refined view of data than Silver tables.
E. Gold tables are more likely to contain truthful data than Silver tables.

正解：C

解説：
Option A is correct. Silver tables are cleaned, validated, and enriched versions of data, often still retaining detailed records. Gold tables are typically business-ready, analytics-focused, and more likely to contain aggregations, dimensional models, and reporting-ready metrics. Official Databricks extract: Silver is associated with "Data cleaning and validation," while Gold is associated with "Dimensional modeling and aggregation." Databricks also states that Gold data is often highly aggregated and tailored for analytics and reporting.

質問 # 82
Data professionals with varying titles use the Databricks SQL service as the primary touchpoint with the Databricks Lakehouse Platform. However, some users will use other services like Databricks Machine Learning or Databricks Data Science and Engineering.
Which of the following roles uses Databricks SQL as a secondary service while primarily using one of the other services?

A. Data analyst
B. Business intelligence analyst
C. Business analyst
D. SQL analyst
E. Data engineer

正解：E

解説：
Data engineers are primarily responsible for building, managing, and optimizing data pipelines and architectures. They use Databricks Data Science and Engineering service to perform tasks such as data ingestion, transformation, quality, and governance. Data engineers may use Databricks SQL as a secondary service to query, analyze, and visualize data from the lakehouse, but this is not their main focus. Reference: Databricks SQL overview, Databricks Data Science and Engineering overview, Data engineering with Databricks

質問 # 83
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