SOL-C01学習指導 & SOL-C01最新対策問題

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Snowflake SOL-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Identity and Data Access Management	15%	- Define the roles that are used in Snowflake 1. Object access by role 2. Explain role hierarchy 3. Privileges 4. Role types 5. Role-Based Access Control (RBAC) - Create a database, explore data, configure parameters, and transfer ownership 1. Run basic SQL commands (EXCLUDE, SELECT, LIMIT) 2. Create and drop schemas 3. Understand context 4. Use database objects (INFORMATION_SCHEMA, PUBLIC SCHEMA) 5. Transfer ownership
Data Protection and Data Sharing	10%	- Outline continuous data protection with Snowflake 1. Time Travel 2. Cloning - Define Snowflake data sharing capabilities 1. Snowflake Marketplace (Search)
Data Loading and Virtual Warehouses	40%	- Create tables in Snowflake and load data into the tables 1. Use Snowsight to identify particular tables 2. Use INSERT statements to load data 3. Use Snowsight to load data 4. File format options 5. Preview the table data 6. Use COPY INTO statements 7. View the table definition - Explain how to work with unstructured data 1. Use of Pre-signed URLs 2. Function argument 3. Directory tables (Enable, Use of SELECT statements) - Explain how to use Snowflake Cortex LLM functions 1. PARSE_DOCUMENT function 2. COMPLETE function 3. CLASSIFY_TEXT function 4. TRANSLATE function
Interacting with Snowflake and the Architecture	35%	- Outline key features and benefits of the Snowflake AI Data Cloud 1. Elastic compute 2. Elastic storage 3. Snowflake layers - Describe how to use the Snowsight user interface 1. Data loading 2. Object browsers 3. Create objects (e.g., databases, schemas, stages) 4. Query history - Outline the key Snowflake user interfaces 1. Snowsight 2. Snowflake Notebooks 3. Worksheets (Python, SQL) - Work with Snowflake Notebooks 1. Run code (SQL, Python, Cell execution status) 2. Notebook sessions 3. Python variable substitution 4. Visualize data using Streamlit - Describe Snowflake objects and how they fit into the Snowflake hierarchy 1. Schemas 2. Databases 3. Views 4. Data types 5. Tables

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Snowflake Certified SnowPro Associate - Platform Certification 認定 SOL-C01 試験問題 (Q78-Q83):

質問 # 78
A data scientist wants to use the COMPLETE function in Snowflake Cortex to perform sentiment analysis on social media posts. They have a table named 'SOCIAL MEDIA POSTS with a column
'POST TEXT' containing the post content. They want to classify each post as either 'Positive',
'Negative', or 'Neutral'. Which of the following SQL statements is the MOST appropriate for this task?

A. Option D
B. Option C
C. Option E
D. Option A
E. Option B

正解：C

解説：
Option E is the most appropriate. It explicitly instructs the COMPLETE function to classify the sentiment and provides the possible classifications (Positive', 'Negative', or 'Neutral'). Combining text with II and using smaller snowflake model to minimize compute cost.

質問 # 79
Which of the following is true about Snowflake's "auto-scale" feature for multi-clustered warehouses?
(Choose any 2 options)

A. It is only available for standard warehouses
B. It automatically adjusts the number of clusters based on query demand
C. It helps optimize resource utilization during variable workloads
D. It requires manual intervention to add or remove clusters

正解：B、C

解説：
Auto-scale enables Snowflake to dynamically start or stop clusters in a multi-cluster warehouse depending on workload pressure. When concurrency rises, Snowflake adds clusters; when demand decreases, it scales back down. This provides seamless performance during peak workloads and efficient resource usage during off- peak times. Auto-scale does not require manual action-it is fully automated once configured. It is not limited to standard warehouses; multi-cluster mode is available in Enterprise Edition and above. Ultimately, auto- scale ensures elasticity and cost-efficient compute allocation.

質問 # 80
You are using Snowflake Notebooks to create a visualization based on data retrieved from a Snowflake table. You have the following code in a cell:

A. The code will execute successfully and display a scatter plot of 'sales' vs. 'profit', but any DML changes in the notebook will not automatically commited until explict commit command executed.
B. The code will fail because the `snowflake.connector' library is not designed for data visualization within Snowflake Notebooks.
C. The code will execute successfully and display a scatter plot, but the changes to the 'SALES DATA' table will need to be committed using a separate `commit' command within another cell.
D. The code will fail because the visualization libraries (e.g., matplotlib) are not pre-installed in Snowflake Notebooks environment and need to be installed first.
E. The code will execute successfully, display a scatter plot of 'sales' vs. 'profit', and automatically commit the changes to the 'SALES_DATA' table.

正解：A、D

解説：
Snowflake Notebooks allow data visualization using libraries like matplotlib, but these libraries are not preinstalled so they need to be installed first. The snowflake.connector is designed for data interaction and not the visualization itself, and a separate commit command is required to persist any DML changes to the database, therefore Option B and E is correct.

質問 # 81
You have a Snowflake external stage configured to access Parquet files in an AWS S3 bucket.
You want to query these Parquet files directly using Snowflake without explicitly loading them into a table. You also want to enable directory tables for this external stage. Which of the following steps are necessary to enable directory tables for the external stage and query the data?

A. Create an external stage pointing to the S3 bucket, then execute 'ALTER STAGE REFRESH;' , then execute 'ALTER STAGE SET ENABLE_DIRECTORY =TRUE;'
B. Create an external stage pointing to the S3 bucket with 'ENABLE DIRECTORY = TRUE specified during stage creation, then refresh the directory table using 'ALTER STAGE REFRESH;'.
C. Create an external stage pointing to the S3 bucket, execute 'ALTER STAGE SET ENABLE_DIRECTORY = TRUE;' , then refresh the directory table using 'ALTER STAGE REFRESH;'.
D. Create an external stage pointing to the S3 bucket with 'ENABLE DIRECTORY = TRUE specified during stage creation, then no refresh is needed. Directory tables are automatically updated.
E. Create an external stage pointing to the S3 bucket, then execute 'ALTER STAGE SET ENABLE_DIRECTORY = TRUE;'

正解：B

解説：
To enable directory tables, you must set 'ENABLE DIRECTORY = TRUE' when creating the stage or using ALTER STAGE. Then, 'ALTER STAGE REFRESH;' populates the directory table with the file metadata. The directory table is not automatically updated and needs to be refreshed periodically.

質問 # 82
What can a schema in Snowflake contain? (Choose any 3 options)

A. Views
B. Virtual warehouses
C. Tables
D. Stage

正解：A、C、D

解説：
Aschemain Snowflake is a logical container inside a database that groups related objects. It can containtables, views,stages,file formats,sequences,streams,tasks, and other database objects. Schemas allow structured organization, privilege management, and separation of workloads.
* Tablesstore structured data.
* Viewsare virtual tables defined by queries on other objects.
* Stagesstore data files for loading/unloading.
Virtual warehouses are compute resources, not database objects, and therefore exist outside schemas.
Warehouses belong to the account-level namespace and are managed independently of databases and schemas.
Schemas play a key role in RBAC and object naming through fully qualified names: database.schema.object.

質問 # 83
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