[2026年01月] 合格 Snowflake SOL-C01 テストエンジンpdf - 完全版無料問題集 [Q34-Q52]

[2026年01月] 合格させるSnowflake SOL-C01テストエンジンPDFで完全版無料問題集

Snowflake Certified SnowPro Associate - Platform Certification練習テスト2026年最新のSOL-C01ストレスなしで合格！

質問 # 34
You are a data provider and want to share a table 'SALES DATA with two different consumer accounts, and 'ACCOUNT B' , using direct shares. However, you want to provide a filtered view of the data to (only sales from region 'East') and the full table to 'ACCOUNT Which of the following approaches is the MOST efficient and secure way to accomplish this?

A. Share the entire table with both accounts and implement row-level security policies on the table, filtering data based on account identifiers.
B. Create a share for containing the 'SALES_DATX table and then create another share for
'ACCOUNT_B' containing the same 'SALES_DATA' table.
C. Create a view 'SALES DATA EAST filtered for 'East' region. Create a share for 'ACCOUNT_X containing Create another share for 'ACCOUNT containing 'SALES DATA.
D. Create a secure view 'SECURE SALES DATA_EAST filtered for 'East' region. create a share for
'ACCOUNT_X containing Create another share for `ACCOUNT B' containing 'SALES DATA'.
E. Create a materialized view `MAT SALES DATA_EAST filtered for 'East' region. create a share for
'ACCOUNT_X containing Create another share for `ACCOUNT B' containing 'SALES DATA'.

正解：D

解説：
Using a secure view is the most efficient and secure approach. Secure views ensure that the filtering logic is encapsulated and not exposed to the consumer, and consumers can't circumvent the filtering. Materialized views require storage and maintenance, making it less efficient. Sharing the entire table with row-level security is valid, but more complex to manage for just this scenario.
Sharing the base table directly to Account A is wrong since you only want to share east region sales and should not create extra shares

質問 # 35
What are the three key layers of Snowflake's architecture?

A. Authentication, Access Control, Cloud Services
B. Database Storage, ODBC Drivers, Virtual Warehouses
C. Data Storage, Query Processing, Cloud Services
D. Data Repository, Compute Clusters, Metadata Management

正解：C

解説：
Snowflake's architecture is built on three primary layers that separate and optimize storage, compute, and services. TheData Storagelayer stores all structured and semi-structured data in Snowflake's optimized, compressed columnar format. Snowflake manages file organization, metadata, and optimizations automatically, ensuring efficient storage management. TheQuery Processinglayer is implemented through virtual warehouses that independently execute queries using MPP (massively parallel processing) compute clusters. These warehouses scale elastically based on workload requirements. TheCloud Serviceslayer manages metadata, authentication, access control, query optimization, and infrastructure orchestration. This separation allows independent scaling of compute and storage, optimizing performance while simplifying administration.

質問 # 36
Which function would you use to determine the sentiment of a customer review in Snowflake Cortex?

A. COMPLETE
B. TRANSLATE
C. SENTIMENT
D. PARSE_DOCUMENT

正解：C

解説：
The SENTIMENT function in Snowflake Cortex analyzes text and returns a numerical sentiment score between -1 and 1. This value indicates whether the text expresses negative, positive, or neutral sentiment. It accepts English-language text and uses Snowflake's managed AI models to infer polarity based on contextual understanding. A negative score represents negative emotion, a positive score represents positive emotion, and values near zero indicate neutrality. It is ideal for customer reviews, feedback analysis, and text mining.
TRANSLATE is used for language translation, COMPLETE for text generation, and PARSE_DOCUMENT for extracting text from documents-not sentiment analysis. SENTIMENT provides fast, in-database AI inference without external model hosting or integration.

質問 # 37
A data engineer is loading data into Snowflake from an external stage. They need to transform data during the load process. Which of the following is the MOST efficient and recommended approach for performing this transformation in Snowflake?

A. Use Snowpipe with a transformation script running on a separate compute instance.
B. Create a View that transforms the data after loading it into a staging table.
C. Use a COPY INTO statement with a SELECT statement that performs the transformations.
D. Download the data from the stage, transform it using a Python script, and then upload it into Snowflake using the Snowflake connector.
E. Use a Stored Procedure to read the data from the stage, transform it, and then insert it into the target table.

正解：C

解説：
The COPY INTO statement with a SELECT statement provides the most efficient and recommended way to transform data during the load process. It leverages Snowflake's compute engine for transformations, avoiding the overhead of external scripts or stored procedures for simple transformations. Using views would transform the data on read, not during load.

質問 # 38
Which options are used in Snowflake notebooks for querying data? (Choose any 3 options)

A. Python
B. Markdown
C. SQL
D. Markup

正解：A、B、C

解説：
Snowflake Notebooks support multiple cell types for development and documentation.Pythoncells allow users to perform data transformations, analysis, and Snowpark operations.SQLcells enable executing Snowflake SQL queries directly against tables and views.Markdowncells support formatted documentation within notebooks-useful for narrative explanations and commentary. The option "Markup" is not a supported cell type in Snowflake Notebooks. These three supported types allow users to blend analytics, code execution, and documentation in a unified environment.

質問 # 39
What are Snowflake customers responsible for?

A. Managing table metadata
B. Software installation and updates
C. Loading, unloading, and managing data
D. Managing the hardware on which Snowflake is installed

正解：C

解説：
As a fully managed cloud data platform, Snowflake is responsible for infrastructure provisioning, hardware, software installation, platform upgrades, scaling, and internal metadata management such as micro-partitions and statistics. Customers do not manage physical hardware or install Snowflake software.
Customers are responsible for their data and its lifecycle within Snowflake. This includes loading data into tables from internal and external sources, unloading data when required, organizing data structures (databases, schemas, tables), defining access controls, and managing how data is used, transformed, and governed. They design schemas and workloads but do not manage the underlying engine. Therefore, "Loading, unloading, and managing data" correctly describes the customer's responsibility.

質問 # 40
What information can be obtained by describing a table in Snowflake? (Choose any 3 options)

A. Column names and data types
B. Table constraints
C. Indexes and keys
D. Table Origin

正解：A、B、C

解説：
The DESCRIBE TABLE (or DESC TABLE) command in Snowflake provides core metadata about table structure. This includescolumn names,data types,nullability, and default values. It also returnsconstraintsif applied, such as PRIMARY KEY, UNIQUE, and CHECK constraints. Snowflake additionally displays information regarding clustering keys and other table characteristics. The command does not show table origin; Snowflake does not track lineage information directly through DESCRIBE TABLE. Indexes in Snowflake are not traditional B-tree indexes-Snowflake uses micro-partition pruning instead-but DESCRIBE TABLE can show clustering keys, which function similarly by enabling optimized data skipping.
Therefore, column definitions, constraints, and keys are all valid outputs of DESCRIBE TABLE.

質問 # 41
You are managing a Snowflake environment that ingests data from various sources, including structured data (CSV files) and semi-structured data (JSON files). You notice that query performance is degrading over time, particularly on tables containing both types of dat

A. Disable automatic query optimization to have more control over query execution plans.
B. You suspect that inefficient data loading practices and suboptimal virtual warehouse configurations are contributing factors. Which of the following actions should you take to address this issue and improve overall query performance? (Select three)
C. Regularly analyze query performance using Snowflake's Query Profile and identify areas for optimization, such as inefficient joins or poorly written filters.
D. Implement micro-batching for data loading, breaking down large files into smaller chunks for parallel processing.
E. Consistently use the smallest virtual warehouse size possible for all data loading tasks to minimize costs.
F. Optimize the virtual warehouse sizing for different workloads, using larger warehouses for complex queries and smaller warehouses for simpler queries.

正解：D、E、F

解説：
Options B, C, and D are the correct choices. Micro-batching (B) improves data loading efficiency by enabling parallel processing. Optimizing virtual warehouse sizing (C) ensures that the appropriate resources are allocated to different workloads, preventing resource contention and improving query performance. Analyzing query performance using Snowflake's Query Profile (D) allows you to identify and address specific bottlenecks in query execution. Option A is not a good practice as very small warehouses might take longer and increase the total cost. Option E is incorrect; you should enable the automatic query optimization instead.

質問 # 42
How do you specify a custom delimiter for a CSV file when using COPY INTO <TABLE>?

A. Using the FIELD_SEPARATOR stage property.
B. Using the DELIMITER_CHAR table property.
C. Using the FIELD_DELIMITER file format option
D. Using the COLUMN_SEPARATOR query parameter

正解：C

解説：
When loading CSV files, Snowflake allows full control over parsing behavior using aFILE FORMATobject or inline file format options. TheFIELD_DELIMITERoption specifies the character that separates fields in the CSV-such as comma (,), pipe (|), semicolon (;), or tab.
Example:
COPY INTO my_table
FROM @my_stage
FILE_FORMAT = (TYPE='CSV' FIELD_DELIMITER='|');
This ensures Snowflake interprets each row correctly based on the custom delimiter.
Incorrect options:
* FIELD_SEPARATORdoes not exist as a stage property.
* DELIMITER_CHARis not a Snowflake file or table option.
* COLUMN_SEPARATORis not a recognized query parameter.
FIELD_DELIMITER is the correct and only method for specifying CSV delimiters in COPY INTO operations.

質問 # 43
What is the default Time Travel retention period for standard tables in Snowflake?

A. 1 day
B. 0 days
C. 7 days
D. 14 days

正解：A

解説：
Standard permanent tables in Snowflake have a default1-day Time Travel retention period. During this period, users may query historical data, restore dropped tables, or clone past states. Higher editions allow extending this period up to 90 days. Temporary and transient tables have 0-day retention by default.

質問 # 44
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 GLOBAL TEMPORARY TABLE AS SELECT FROM existing_table;
B. CREATE LOCAL TEMPORARY TABLE AS SELECT FROM existing_table;
C. CREATE TEMP TABLE AS SELECT FROM existing_table;
D. CREATE TABLE AS SELECT FROM existing_table;
E. CREATE VOLATILE TABLE AS SELECT FROM existing_table;

正解：C

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 # 45
You have a table 'PRODUCT_PRICES' defined as 'CREATE TABLE PRODUCT_PRICES (PRODUCT_ID INT, PRICE LAST UPDATED TIMESTAMP NTZ)'. You want to insert new prices for some products, but only if the new price is different from the existing price. If the price is the same, you want to update the 'LAST UPDATED' timestamp. Which of the following approaches would be the most efficient in Snowflake to achieve this?

A. First 'INSERT all the new prices into a temporary table, then use a 'JOIN' with the original table to identify the rows that need to be updated or inserted.
B. Use `MERGE statement to update the 'LAST UPDATED timestamp and insert new records if a record for the 'PRODUCT ID does not exist.
C. Create a new table with all the product IDs that need to be updated and use 'INSERT OVERWRITE to replace original table.
D. Perform a 'SELECT statement for each 'PRODUCT_ID to check the existing price, then either
'INSERT or 'UPDATE accordingly.
E. Use a stored procedure that iterates through the new data and performs 'INSERT or 'UPDATE statements based on the existing price.

正解：B

解説：
Option C is the most efficient because the 'MERGE statement is specifically designed for scenarios where you need to conditionally insert or update data based on a join condition. It avoids the overhead of multiple `SELECT statements or a stored procedure iterating through the data. A and B perform row-by- row operations which is slow in Snowflake. Although D is valid, it requires you to create temporary tables, populate and then join against the original table, which takes more time and code. Option E overwrites the whole table which is not needed since we only need to update records if a record for the 'PRODUCT_ID' does not exist.

質問 # 46
What does "warehouse scaling up/down" refer to in Snowflake?

A. Adjusting the number of clusters in a multi-cluster warehouse.
B. Changing the size of the warehouse (e.g., from Small to Medium or Vice Versa).
C. Moving data between different storage locations
D. Changing the region of the warehouse

正解：B

解説：
Scalingup or downrefers tovertical scaling, meaning the warehouse's compute size is increased or decreased.
For example, moving fromSmall # Medium # Largeincreases CPU, memory, and I/O capacity, enabling faster processing for compute-intensive workloads.
Vertical scaling improves single-query performance, large ETL jobs, complex joins, or transformations. It does not improve concurrency unless multi-cluster mode is also used.
Horizontal scaling (scaling out/in), by contrast, adjusts thenumber of clustersand is used for concurrency.
Region selection is fixed at account creation and cannot be changed by resizing a warehouse. Storage movement is unrelated to compute rescaling.

質問 # 47
A data scientist has developed a Streamlit application within a Snowflake Notebook to perform predictive analytics on customer churn. The application uses a pre-trained machine learning model stored as a Snowflake stage object. The model takes several customer features as input, which are stored in a Snowflake table called 'CUSTOMER FEATURES'. The data scientist needs to ensure that the model is loaded efficiently and that the inference is performed securely within the Snowflake environment, minimizing data movement.
Which of the following approaches would be the MOST efficient and secure for loading the pre- trained model and performing the inference within the Snowflake environment using a Streamlit application?

A. Implement Snowpark and load the model from the Snowflake stage, transforming and sending the data to the model with Snowpark session in Snowflake, leveraging Streamlit for visualization.
B. Create a Snowflake external function (UDF) that loads the pre-trained model from the Snowflake stage and performs the inference. Call this UDF from the Streamlit application, passing the customer features as input.
C. Create a Snowflake stored procedure that loads the pre-trained model from the Snowflake stage and performs the inference using SQL commands (if the model is compatible). Call this stored procedure from the Streamlit application.
D. Download the pre-trained model from the Snowflake stage to the Streamlit application's local storage and perform the inference using a standard Python machine learning library.
E. Use Streamlit's caching capabilities Cst.cache_resource') to load the pre-trained model from the Snowflake stage into the Streamlit application's memory. Then, perform the inference directly within the Streamlit application.

正解：A、B

解説：
Options B and E are the most appropriate for efficiency and security. Creating a Snowflake UDF or Snowpark session (E) keeps the data and model processing within the Snowflake environment, minimizing data movement and leveraging Snowflake's compute resources. UDFs or Snowpark session provide a secure and efficient way to perform the inference. Downloading model is not scalable (A), simple caching might be inefficent (C), storing procedure is not suitable as its not SQL based (D).

質問 # 48
What is the highest level object in the Snowflake object hierarchy?

A. Schema
B. Account
C. Virtual Warehouse
D. Database

正解：B

解説：
TheAccountis the top-level Snowflake container encompassing:
* All databases
* All schemas
* All compute resources (warehouses)
* All roles, users, and governance structures
All other objects exist within the Account context.

質問 # 49
A data engineer needs to grant SELECT privileges on all tables within a newly created schema named 'SALES DATA' to a role called 'ANALYST ROLE'. However, new tables may be added to this schema in the future. What is the most efficient and secure way to achieve this, ensuring that future tables automatically inherit the necessary permissions?

A. Create a custom role that inherits from 'ANALYST ROLE and grant 'SELECT privilege on all tables to this new role.
B. Grant SELECT privilege to `ANALYST ROLE directly on each table using `GRANT SELECT ON TABLE ... TO ROLE ANALYST ROLE;' after table creation.
C. Grant 'SELECT privilege to the PUBLIC role on all tables within the 'SALES_DATX schema.
D. Grant the USAGE privilege on the database containing the schema and SELECT privilege on
`FUTURE GRANTS for tables in 'SALES DATA' to the 'ANALYST ROLE using 'GRANT USAGE ON DATABASE TO ROLE ANALYST ROLE; GRANT SELECT ON FUTURE TABLES IN SCHEMA SALES DATA TO ROLE ANALYST ROLE;'
E. Use a stored procedure to automatically grant the SELECT privilege to 'ANALYST ROLE whenever a new table is created in the 'SALES DATA' schema.

正解：D

解説：
Using GRANT SELECT ON FUTURE TABLES' ensures that any new tables created in the schema automatically inherit the SELECT privilege for the specified role. Option A requires manual intervention for each new table. Options C and E are unnecessarily complex. Option D poses a security risk by granting privileges to the PUBLIC role.

質問 # 50
A junior data engineer is having trouble connecting to Snowflake from a Snowflake Notebook using Snowpark. They receive the following error:
`snowflake.snowpark.exceptions.SnowparkClientException:
(250001 ): Authentication failed: Account is not found. Ensure your account identifier is correct.
Account identifiers are case-sensitive.' They have verified their username, password, and database/schema names. Which of the following is the MOST likely cause of the error and the correct solution?

A. The Snowflake JDBC driver is not installed correctly in the Snowflake Notebook environment.
Reinstall the Snowflake JDBC driver using 'pip install snowflake-connector-python'.
B. The Snowflake service is currently experiencing an outage. Check the Snowflake Trust site for service status updates.
C. The account identifier specified in the connection parameters is incorrect or case-sensitive. Verify the account identifier using 'SHOW VARIABLES LIKE 'ACCOUNT _ IDENTIFIER'` in Snowflake and update the connection parameters accordingly.
D. The user's role does not have sufficient privileges to access the Snowflake account. Grant the user the ACCOUNTADMIN role.
E. Snowflake Notebooks do not support password authentication. The user must configure key pair authentication.

正解：C

解説：
The error message clearly indicates an issue with the account identifier. Account identifiers are case- sensitive, and the error message specifically advises checking it. Option A is less likely since the error is about the account identifier , not general access. The JDBC driver is not directly used with Snowpark (C). An outage (D) is possible but less likely as the specific error points to the account identifier. Password authentication is supported (E).

質問 # 51
Which two actions are possible within a single Snowflake Notebook session? (Choose two)