[2026年01月15日] 365日無料更新SOL-C01知能問題集をゲット [Q14-Q37]

[2026年01月15日] 365日無料更新SOL-C01知能問題集をゲット

ベスト品質のSnowflake SOL-C01試験問題

質問 # 14
What is the Snowsight Query Profile used for?

• A. To visualize and analyze query performance
• B. To manage data loading processes
• C. To create new database objects
• D. To execute SQL queries

正解：A

解説：
The Snowsight Query Profile is a powerful diagnostic tool that provides a visual breakdown of how Snowflake executed a query. Its primary purpose is to help users visualize and analyze query performance. It displays execution steps, including scan operations, join strategies, pruning results, aggregation methods, and data movement between processing nodes.
The profile shows metrics such as execution time per step, partition pruning effectiveness, bytes scanned, and operator relationships. This allows developers, analysts, and DBAs to identify bottlenecks-such as unnecessary full-table scans, non-selective filters, or inefficient joins-and tune SQL accordingly.
Query Profile does not execute queries; execution happens in worksheets or programmatic interfaces. It does not create objects or manage data loading; those tasks involve separate SQL commands and UI interfaces.
Overall, Query Profile is essential for performance tuning, helping teams reduce compute costs, optimize warehouse sizing, and improve query efficiency.

質問 # 15
What information can be accessed using the Snowsight Monitoring tab?

• A. Database schema changes history
• B. Database Time Travel snapshots
• C. Query execution history
• D. Virtual warehouse usage metrics

正解：D

解説：
The Snowsight Monitoring tab provides a centralized view of virtual warehouse usage metrics, enabling administrators and developers to evaluate how compute resources are being consumed. This includes critical insights such as credit usage, query load, concurrency levels, average queue times, execution durations, and auto-scaling activity (for multi-cluster warehouses). These metrics help determine whether a warehouse is correctly sized, whether concurrency issues are occurring, or whether workloads require scaling up or adding clusters.
Query history is available in a different section-"Activity # Query History"-not under Monitoring. Time Travel snapshots are not visualized within Monitoring; Time Travel is controlled via retention parameters and accessed with SQL (AT/BEFORE clauses). Schema change history is also not part of Monitoring and instead is discoverable through ACCOUNT_USAGE or specific metadata views.
The Monitoring tab exists specifically to help evaluate warehouse performance and resource consumption, enabling optimization of compute spending and better workload management.

質問 # 16
When loading data into a table using the COPY INTO command, what is the default error-handling setting?

• A. SKIP_FILE
• B. VALIDATE
• C. CONTINUE
• D. ABORT_STATEMENT

正解：D

解説：
By default, Snowflake usesON_ERROR = ABORT_STATEMENTfor COPY INTO. This means that if a single row fails to load due to issues such as type mismatches or malformed data, Snowflake immediately stops the entire load operation. This behavior ensures data integrity by preventing partial or incomplete loads when errors occur.
Other error-handling modes must be explicitly specified: CONTINUE skips erroneous rows, SKIP_FILE skips whole files with errors, and VALIDATE is part of the validation mode feature, not a default COPY behavior.
Thus, unless explicitly changed, COPY INTO always uses ABORT_STATEMENT.

質問 # 17
You are using the Snowflake web interface to execute SQL queries. You want to save a specific query for future use. Which of the following methods allows you to effectively save and organize your SQL queries within Snowflake's webUIand how it is best used?

• A. Store the query as a Stored Procedure. Useful when the query is part of a larger process and needs to be executed programmatically with parameters.
• B. Save the query directly within a Worksheet. Worksheets in Snowflake are designed to store and organize SQL queries; you can create multiple worksheets and name them descriptively to manage your scripts effectively. This allows saving, organizing, and re-running them directly from the Snowflake UI.
• C. Use the 'Create View' command in SQL to save the query's result set as a view.
• D. Export the query as a JSON file using the 'Export' feature. Best to use when needed to store the queries and not running.
• E. Download the query as a text file using the 'Download' option from the worksheet menu. Re- uploading will be required, when required.

正解：B

解説：
Worksheets in Snowflake are specifically designed for saving and organizing SQL queries directly within the Snowflake web interface. You can create multiple worksheets, name them appropriately, and save your SQL scripts within them. This allows easy access and re- execution of your queries. While the other options are valid for other purposes (downloading, creating views, creating stored procedures), they are not the primary and most efficient way to save queries for reuse within the Snowflake I-Jl.

質問 # 18
What is a Markdown cell in Snowflake Notebooks?

• A. A cell that contains data in more than one computer languages
• B. A cell that is nested in other cells
• C. A cell that allows the user to format the cell text
• D. A cell that contains older data

正解：C

解説：
In Snowflake Notebooks, a Markdown cell is a non-executable cell type used for formatted text. It allows the user to format text using Markdown syntax, including headings, lists, tables, emphasis, inline code, and links.
This makes it possible to document the analysis, describe steps, and provide commentary alongside SQL and Python cells, improving clarity and collaboration.
Markdown cells do not execute code in "more than one computer language"; they are not code cells at all.
Notebook cells are not nested; each cell exists as a separate element in the notebook. There is no concept of
"older data" associated specifically with Markdown cells; they simply store text content defined by the user.

質問 # 19
What command can be used to load data from an external stage to a table?

• A. GET
• B. PUT
• C. INSERT INTO
• D. COPY INTO

正解：D

解説：
TheCOPY INTOcommand is Snowflake's primary bulk-loading mechanism for ingesting data from stages (internal or external) into tables. It can read from Amazon S3, Azure Blob Storage, Google Cloud Storage, or internal Snowflake stages. COPY INTO supports file formats (CSV, JSON, Parquet), pattern matching, error handling, transformations, and validation modes.
PUT uploads filesfrom a local machineto an internal stage-not from stage to table.
GET downloads files from an internal stage to a local system.
INSERT INTO is for row-by-row inserts and is not designed for large file ingestion.
Therefore, COPY INTO is the correct and recommended method for loading stage files into Snowflake tables.

質問 # 20
You have a role hierarchy where 'ROLE A' has been granted to 'ROLE B', and 'ROLE B" has been granted to User 'USER X' 'ROLE R has 'SELECT privilege on 'TABLE_I' and 'INSERT' privilege on 'TABLE 2'. Assume that 'USER_X' is currently using the 'ACCOUNTADMIN' role.
Which of the following statements accurately describe the privileges available to 'USER X'?
(Choose two)

• A. ` USER_X' can SELECT from only when the active role is set to
• B. USER_X can SELECT from 'TABLE_I" and INSERT into 'TABLE_2 regardless of the active role.
• C. USER X' cannot SELECT from 'TABLE 1 ` or INSERT into STABLE_2 because the privileges are not directly granted to the user.task, as the active role is set to ACCOUNTADMIW.
• D. USER_X' can SELECT from ` TABLE _ 1 ` and INSERT into ` TABLE _ 2 only when the active role is set to 'ROLE_B'

正解：D

解説：
Option B: The privileges associated with ` ROLE_A' are inherited by , and then by 'USER_X' when ` ROLE_B' is activated. Thus, USER_X can select from table 1 and insert into table_2 when active role is Role B. Option E: Because Accountadmin can do almost anything, any user in a session with active role accountadmin can perform nearly any operation. Option A is incorrect because user must use role B to select from table 1. Option C is incorrect because to select/insert, user must use role B or ACCOUNTADMIN .Option D is incorrect because the role hierarchy allows privilege inheritance. The privileges granted to roles are accessible only when those roles are active, and USER_X can perform any account related task with ACCOUNTADMIN.

質問 # 21
A data scientist wants to use a library, 'special_function', within a Snowflake Notebook, but that library is not pre-installed in the Snowflake environment. The data scientist does not have ACCOUNTADMIN privileges. Which of the following methods could be used to make the
'special_function' library available for use within the notebook?

• A. Create a local virtual environment within the Snowflake Notebook's file system, install the library into the virtual environment, and then activate the virtual environment before using the library.
• B. Upload the library file ('special_function.py' or similar) to a Snowflake stage, and then import the library within the notebook using a relative path to the stage file location.
• C. Create a zip file containing `special_function' and its dependencies, upload it to an internal stage, and then use in the notebook to make the package available to the Snowpark session.
• D. Use '!pip install special_function' in a notebook cell. This will install the library globally for all users on the Snowflake account.
• E. Request an ACCOUNTADMIN to create a Snowflake Anaconda channel using 'CREATE ANACONDA INTEGRATION and upload the library to that channel.

正解：C

解説：
The correct approach is to package the library and upload it to a stage, then use This allows users without ACCOUNTADMIN to use custom libraries. install' (A) requires specific permissions that the user lacks, and installs globally, which is undesirable. Creating an Anaconda integration (B) also requires ACCOUNTADMIN. Virtual environments (C) are not the typical way Snowflake handles external dependencies in notebooks and there are limitation regarding the location.
Uploading directly and importing via relative path (D) can create import errors and may not work with all library types.
Packages from a stage should be made available through

質問 # 22
Consider the following Snowflake warehouse definition: 'CREATE OR REPLACE WAREHOUSE MY WAREHOUSE WITH WAREHOUSE SIZE = 'MEDIUM' WAREHOUSE TYPE = 'STANDARD' AUTO SUSPEND = 300 AUTO RESUME = TRUE MIN CLUSTER_COUNT = 1 MAX CLUSTER_COUNT = 3 SCALING_POLICY = 'ECONOMY;' Which of the following statements are TRUE regarding this warehouse? (Select all that apply)

• A. The 'WAREHOUSE_TYPE = 'STANDARD" setting is invalid and will cause an error.
• B. The warehouse will always have at least one active cluster.
• C. The warehouse can scale up to a maximum of three clusters to handle concurrent queries.
• D. The warehouse is optimized for minimizing costs rather than maximizing performance when scaling.
• E. The warehouse will automatically suspend after 5 minutes of inactivity.

正解：B、C、D、E

解説：
The warehouse will auto-suspend after 300 seconds (5 minutes) of inactivity (A). = 1 ` ensures at least one active cluster (B). = 3' allows scaling to three clusters (C). 'SCALING_POLICY =
'ECONOMY" prioritizes cost savings over immediate performance during scaling (D).
'WAREHOUSE_TYPE = 'STANDARD'` is a valid setting, it defines the warehouse as using the standard model for resource management.

質問 # 23
A data scientist needs to create a temporary table in Snowflake to perform some data analysis.
The table should only be accessible within their current session and should be automatically dropped at the end of the session. Which of the following SQL statements is the CORRECT way to create such a table?

• A. CREATE TEMP TABLE AS SELECT FROM existing_table;
• B. CREATE VOLATILE TABLE AS SELECT FROM existing_table;
• C. CREATE GLOBAL TEMPORARY TABLE AS SELECT FROM existing_table;
• D. CREATE TABLE AS SELECT FROM existing_table;
• E. CREATE LOCAL TEMPORARY TABLE AS SELECT FROM existing_table;

正解：A

解説：
The 'CREATE TEMP TABLE statement is the correct way to create a temporary table in Snowflake that is only visible within the current session and is automatically dropped when the session ends. 'GLOBAL TEMPORARY TABLE' and 'LOCAL TEMPORARY TABLE' are not valid Snowflake syntax. 'CREATE TABLE without 'TEMP' creates a permanent table. 'VOLATILE applies to functions, not tables.

質問 # 24
You have a Snowflake table named `CUSTOMER DATA' with columns `CUSTOMER ID', `NAME,
'CITY, and 'TRANSACTION DATE. You need to implement a data masking policy that masks the
'NAME column for all users except those with the 'ACCOUNTADMIN' role. Additionally, you want to implement a row-level security policy that filters the data based on the `CITY column, allowing users with the 'CITY MANAGER role to only see data for their assigned city. How would you implement these policies?

• A. Create a masking policy using regular expressions to filter the data. Create a stored procedure to filter the data based on the 'CITY' column for certain users.
• B. Create masking policy using the 'TRANSFORM' function to mask the 'NAME' column, and create a row access policy to filter rows based on 'CITY'. Ensure the row access policy uses a mapping table between CITY_MANAGER and CITY.
• C. Create a view on top of the 'CUSTOMER_DATA' table and implement the masking and row-level security logic within the view definition using CASE statements based on CURRENT ROLE() and CURRENT USER().
• D. Create a stored procedure with 'EXECUTE AS OWNER to filter the data based on the 'CITY column for certain users, and masking policy to use `SHA256' on the 'NAME column
• E. Create a masking policy for the `NAME' column using a CASE statement that checks the CURRENT ROLE() and applies the mask if the role is not ACCOUNTADMIW. create a security policy for row-level filtering on the 'CITY column, checking the and to determine the appropriate filter condition. Apply the row access policy to the table.

正解：B、E

解説：
Options A and D correctly implements masking and row-level security. Option A utilizes masking policy for the 'NAME column that considers the `ACCOUNTADMIN' role. And utilizes a row-level security policy (row access policy) that consider 'CITY_MANAGER and 'CITY' for filtering. Option D utilizes `TRANSFORM' to transform and mask the 'NAME' column and implements row access policy which includes Mapping table between CITY and CITY_MANAGER. Option B,C, and E are incorrect as they either create a view, stored procedure and/or regular expressions, which are not the best practices of implementing masking and row-level security in Snowflake.

質問 # 25
A data engineer is building a Snowflake Notebook to perform some data transformations. The notebook interacts with a Snowflake table named 'SALES DATA' which contains columns like
'ORDER ID', 'PRODUCT ID, 'SALE DATE, and `SALE AMOUNT. The engineer wants to use a Streamlit application to visualize the daily sales trends based on the transformed data. However, they are facing challenges with authenticating the Streamlit application to Snowflake and efficiently retrieving the data. Which of the following approaches would provide the most secure and efficient way to authenticate Streamlit to Snowflake and load the necessary data?

• A. Using Snowflake's OAuth integration and leveraging Streamlit's ability to handle authentication flows to securely obtain and use temporary credentials.
• B. Using a Snowflake access token generated outside of Streamlit and passing it as a Streamlit secret or environment variable. Leverage Snowflake Connector for Python in Streamlit to authenticate.
• C. Creating a Snowflake user with extremely broad privileges and storing those credentials as Streamlit secrets.
• D. Granting public access to the 'SALES_DATA' table and querying it directly from Streamlit without authentication.
• E. Hardcoding Snowflake credentials (username and password) directly within the Streamlit application script.

正解：A、B

解説：
Option B is correct because it uses a more secure approach than hardcoding credentials by leveraging Streamlit secrets for access token management. Option E is also correct as Snowflake OAuth integration allows leveraging standard authentication flows for secure credential handling within the streamlit application. Hardcoding credentials (A), creating overly permissive users (C) and granting public access (D) are all significant security risks. Using temporary credentials is the most secure best practice.

質問 # 26
You are working with Snowflake Cortex and the COMPLETE function to generate marketing copy from product specifications. You have a table `PRODUCT SPECS containing 'PRODUCT NAME,
'KEY FEATURES, and TARGET AUDIENCE columns. You want to generate a concise and engaging marketing tagline for each product. Given that 'SNOWFLAKE.ML.COMPLETE is implemented as a secure external function, what are the MINIMUM set of steps to grant the necessary permissions and execute the function successfully (select all that apply)?

• A. Grant the USAGE privilege on the SNOWFLAKE schema to the role executing the query using:
  GRANT USAGE ON SCHEMA SNOWFLAKE TO ROLE ,
• B. Grant the EXECUTE TASK privilege on the account to the role executing the query using. `GRANT EXECUTE TASK ON ACCOUNT TO ROLE
• C. Grant the USAGE privilege on the database containing the SNOWFLAKE schema to the role executing the query using: GRANT USAGE ON DATABASE SNOWFLAKE SAMPLE DATA TO ROLE
• D. Ensure external functions are enabled at the account level: ALTER ACCOUNT SET ENABLE EXTERNAL FUNCTIONS-TRUE;
• E. Grant OWNERSHIP on the SNOWFLAKE.ML.COMPLETE to the role executing the query using:GRANT OWNERSHIP ON FUNCTION SNOWFLAKE .ML. COMPLETE TO ROLE;

正解：A、C、D

解説：
The COMPLETE function, being implemented as an external function, requires specific privileges.
Granting USAGE on the schema (A) and database (C) containing the function is essential for accessing it. Also, external functions must be enabled at the account level(D). EXECUTE TASK is unrelated to external function execution. OWNERSHIP is not needed, USAGE provides sufficient access. A and C are prereqisite to use the functions and as well Snowflake ML complete being implemented as External function, the flag ENABLE EXTERNAL FUNCTIONS should be turned on, so the correct options are

質問 # 27
What are the steps to create a new schema in Snowsight?

• A. Data >> Add Data >> Create Schema
• B. Data >> Database >> Create Schema
• C. Data Product >> Create Schema
• D. Data >> Create Schema

正解：B

解説：
To create a new schema in Snowsight, users must follow the hierarchical navigation of Snowflake's object structure. The correct sequence is:
Data # Database # Create Schema
Steps:
* Open theDatatab in the left navigation.
* Select aDatabasewhere the schema will be created.
* ChooseCreate # Schema.
* Enter the schema name and (optionally) description.
* ClickCreate.
Incorrect options:
* "Add Data" is used for loading files, not creating schema objects.
* "Data # Create Schema" is incomplete because Snowflake must knowwhich databasethe schema belongs to.
* "Data Product" is unrelated to object creation.
Thus, Snowflake requires explicit database context before schema creation.

質問 # 28
You are designing a data pipeline in Snowflake to process streaming data from Apache Kafka.
You need to choose the appropriate virtual warehouse size. The data arrives at a rate of 100,000 messages per second, and each message is approximately IKB. The processing involves complex transformations and aggregations. Which of the following considerations are MOST critical when determining the optimal warehouse size?

• A. Only the total volume of data (100MB/second) needs to be considered. Choose the smallest warehouse that can handle this volume.
• B. The number of concurrent users accessing the processed data is the primary factor. Choose a warehouse size that scales linearly with the number of users.
• C. Always choose the largest available warehouse (X-Large or larger) to ensure optimal performance, regardless of the actual processing requirements.
• D. The complexity of the transformations and aggregations performed on the data significantly impacts the required compute resources. Choose a larger warehouse initially and monitor performance.
• E. The frequency of data arrival (100,000 messages/second) requires a warehouse with sufficient concurrency to avoid message backlog. Choose a warehouse size based on concurrency needs.

正解：D、E

解説：
The complexity of transformations and the frequency of data arrival both play crucial roles in determining warehouse size. Complex transformations require more compute power, and high data arrival rates require higher concurrency to avoid backlog. Option A is incorrect because it only considers data volume. Option C is relevant for querying the processed data, not for processing the streaming data itself. Option D is not cost-effective.

質問 # 29
Which package is needed for Python worksheet?

• A. Pandas
• B. NumPy
• C. Snowpark
• D. Matplotlib

正解：C

解説：
Snowflake's Python Worksheets require theSnowpark for Pythonpackage. Snowpark integrates deeply with Snowflake's compute engine, enabling DataFrame operations, UDF creation, ML workflows, and direct execution of Python logic inside Snowflake compute.
While additional libraries like Pandas, NumPy, and Matplotlib may be available in worksheets, they arenot requiredto enable the Python execution environment. Snowpark is the core computational interface that connects Python to Snowflake's data and compute layers.
It enables:
* Distributed data processing
* Transformations expressed in Python but executed inside Snowflake
* Access to Snowflake tables as DataFrames
* Integration with Snowpark ML and Cortex functions
Thus, Snowpark is the essential package for Python worksheet support.

質問 # 30
What happens when you execute the following command in Snowflake?
DROP SCHEMA IF EXISTS marketing_schema;

• A. The schema "marketing_schema" is created if it does not exist
• B. The schema "marketing_schema" is dropped even if it contains tables
• C. The schema "marketing_schema" is dropped only if it exists
• D. An error is returned if the schema does not exist

正解：C

解説：
The SQL command DROP SCHEMA IF EXISTS marketing_schema; instructs Snowflake to drop the specified schema only if it already exists in the database. The inclusion of the IF EXISTS clause prevents Snowflake from returning an error when the schema is not found. If the schemadoesexist, Snowflake drops it along with all objects it contains, unless a RESTRICT clause is enforced (RESTRICT is the default when not explicitly specified, but Snowflake also supports CASCADE for forcing deletion of all objects). Importantly, this command never creates a schema; it strictly performs a conditional drop. In addition, Snowflake supports Time Travel, so the dropped schema can typically be restored-using UNDROP SCHEMA-within the defined retention window. Therefore, the correct interpretation is that the schema is dropped only if it exists, and no error is thrown otherwise.

質問 # 31
A data engineer needs to create a new database named `SALES DATA for storing sales transactions. They want to ensure that only users with the 'DB_ADMIN' role can manage the database. After creating the database, the engineer needs to grant the `SELECT privilege on all tables within the `SALES DATA.PUBLIC' schema to a role named 'ANALYST'. Which of the following is the MOST secure and efficient sequence of SQL commands to accomplish this?

• A. CREATE DATABASE SALES DATA; USE DATABASE SALES DATA; GRANT OWNERSHIP ON DATABASE SALES DATA TO ROLE DB ADMIN; GRANT SELECT ON FUTURE TABLES IN SCHEMA PUBLIC TO ROLE ANALYST;
• B. CREATE DATABASE SALES DATA; GRANT OWNERSHIP ON DATABASE SALES DATA TO ROLE DB ADMIN; GRANT SELECT ON ALL TABLES IN SCHEMA SALES DATA.PUBLIC TO ROLE ANALYST;
• C. CREATE DATABASE SALES DATA; GRANT OWNERSHIP ON DATABASE SALES DATA TO ROLE DB ADMIN; GRANT SELECT ON FUTURE TABLES IN SCHEMA SALES DATA.PUBLIC TO ROLE ANALYST;
• D. CREATE DATABASE SALES DATA; GRANT OWNERSHIP ON DATABASE SALES DATA TO ROLE DB ADMIN; GRANT SELECT ON SCHEMA SALES DATA.PUBLIC TO ROLE ANALYST, GRANT SELECT ON ALL TABLES IN SCHEMA SALES DATA.PUBLIC TO ROLE ANALYST;
• E. CREATE DATABASE SALES DATA; GRANT OWNERSHIP ON DATABASE SALES DATA TO ROLE DB ADMIN; USE DATABASE SALES DATA; GRANT SELECT ON ALL TABLES IN SCHEMA PUBLIC TO ROLE ANALYST;

正解：C

解説：
Option B is the most secure and efficient. Granting OWNERSHIP on the database ensures DB_ADMIN can manage it. Using 'GRANT SELECT ON FUTURE TABLES' ensures that any new tables created in the `PUBLIC' schema will automatically have the 'SELECT privilege granted to the ANALYST' role. This eliminates the need to manually grant privileges each time a new table is created. Option A only grants access to existing tables. Options C and E are inefficient because they attempt to grant access to the schema itself, which is not the correct approach for controlling access to the tables within it. Option D requires using the database explicitly. Option A and E does not have future grants

質問 # 32
A financial institution needs to maintain a record of all changes made to its customer data for regulatory compliance. They are using Snowflake and want to leverage Time Travel and Fail-safe for data protection. Which of the following strategies would BEST satisfy this requirement, considering both recoverability and compliance?

• A. Set to the maximum allowed value (90 days for Business Critical or as configured) and regularly clone the database to a separate, secure location for long-term archival.
• B. Leverage Time Travel within its configured retention period for recovering from user errors or data corruption and understand that Fail-safe is for Snowflake's disaster recovery, not for direct customer use or data retention.
• C. Rely solely on Fail-safe for long-term data retention and recovery.
• D. Set appropriately (e.g. 90 days) and regularly back up data to external storage and create granular access roles.
• E. Set to the maximum value for the account. Do nothing else as this will satisfy the audit requirement, because snowflake automatically handles data recovery with continuous backups.

正解：A、B

解説：
Option B provides a comprehensive solution by using Time Travel for the short term within a retention period. After the retention period expires, Fail-safe kicks in (handled by Snowflake). Fail- safe is not directly accessible to the user. A clone is created regularly for archival, providing a long-term record for compliance. Option E accurately describes Fail-safe as a disaster recovery measure rather than a tool for direct data recovery by the customer.

質問 # 33
What is a key characteristic of the Snowflake architecture's Cloud Services Layer?

• A. It handles security and metadata management.
• B. It stores all customer data.
• C. It manages virtual warehouses.
• D. It provides the user interface for Snowsight.

正解：A

解説：
The Cloud Services Layer is the coordination and control layer of Snowflake's architecture. One of its primary responsibilities is managing security, metadata, authentication, and system-wide services. This layer handles user authentication, role-based access control, metadata services (such as table structures, micro- partition metadata, statistics), query parsing, optimization, execution coordination, and transaction management.
It does not store customer data; storage is handled by the Database Storage Layer using micro-partitions. It does not manage virtual warehouses directly; warehouses are part of the Compute Layer. While Snowsight is a UI that interacts with the Cloud Services Layer, the interface itself is not part of the architectural layer.
The Cloud Services Layer essentially acts as the "brain" of Snowflake, ensuring the platform is consistent, secure, optimized, and able to scale operations intelligently across compute clusters and cloud-native storage environments.

質問 # 34
Which of the following statements are true about Zero-Copy Cloning in Snowflake? (Select TWO)

• A. It duplicates the underlying data storage.
• B. It creates a writable copy of an object.
• C. It is immediate and metadata-only operation.
• D. It requires significant additional storage.

正解：B、C

解説：
Zero-Copy Cloning is ametadata-only operation, creating instantaneous clones without copying underlying data. Clones are fully writable and operate independently. Snowflake only allocates storage when data diverges due to modifications. It does not require significant storage nor duplicate physical data.

質問 # 35
You are loading data from a CSV file stored in an AWS S3 bucket into a Snowflake table. The CSV file uses a custom delimiter and contains a date field that needs to be explicitly formatted during the load. Which combination of 'COPY INTO* options BEST addresses these requirements? Assume an existing stage named 's3_stage'.

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

正解：A

解説：
Option A correctly specifies the custom field delimiter (FIELD DELIMITER = and the explicit date format (DATE FORMAT = 'YYYY-MM-DD") to handle the CSV loading and date transformation requirements effectively. Option B uses 'DATE_FORMAT = 'AUTO" , which might not correctly parse the date if it's not in a standard format. Options C, D and E don't contain 'ON ERROR to ensure that, in case of error the load continues

質問 # 36
What are compute resources called in Snowflake?

• A. Virtual Machines
• B. Compute Clusters
• C. Data Nodes
• D. Virtual Warehouses

正解：D

解説：
Snowflake compute resources are referred to asVirtual Warehouses. A virtual warehouse is a cluster of compute nodes that executes SQL queries, performs DML operations (INSERT/UPDATE/DELETE), and runs data loading or transformation tasks.
Virtual Warehouses provide:
* Dedicated compute isolation
* Independent scaling (resize at any time)
* Concurrency support through multi-cluster mode
* Auto-suspend and auto-resume for cost efficiency
While Virtual Warehouses consist of compute clusters under the hood, Snowflake abstracts the underlying VM and node architecture, exposing only the warehouse construct to users. This ensures simplicity and avoids operational burdens such as node management.
Incorrect terms like Data Nodes or Virtual Machines represent underlying infrastructure concepts not exposed to end users.

質問 # 37
......

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検証された材料は決まってこれ！SOL-C01：https://drive.google.com/open?id=1m57m4pgE9DO9TdK_t-EgyBYAT32Rq197