検証するSOL-C01｜正確的なSOL-C01問題トレーリング試験｜試験の準備方法Snowflake Certified SnowPro Associate - Platform Certification関連資格知識

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Snowflake SOL-C01 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Identity and Data Access Management: This domain focuses on Role-Based Access Control (RBAC) including role hierarchies and privileges, along with basic database administration tasks like creating objects, transferring ownership, and executing fundamental SQL commands.
トピック 2	Data Protection and Data Sharing: This domain addresses continuous data protection through Time Travel and cloning, plus data collaboration capabilities via Snowflake Marketplace and private Data Exchange sharing.
トピック 3	Interacting with Snowflake and the Architecture: This domain covers Snowflake's elastic architecture, key user interfaces like Snowsight and Notebooks, and the object hierarchy including databases, schemas, tables, and views with practical navigation and code execution skills.
トピック 4	Data Loading and Virtual Warehouses: This domain covers loading structured, semi-structured, and unstructured data using stages and various methods, virtual warehouse configurations and scaling strategies, and Snowflake Cortex LLM functions for AI-powered operations.

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Snowflake Certified SnowPro Associate - Platform Certification 認定 SOL-C01 試験問題 (Q41-Q46):

質問 # 41
You are tasked with deploying a new data application to Snowflake. This application requires several schemas for staging, transformation, and reporting. What is the recommended approach to create these schemas using Infrastructure as Code (laC) and ensuring consistency across multiple Snowflake environments (DEV, TEST, PROD)?

A. Copy the DDL (Data Definition Language) scripts generated by the Snowflake UI after manually creating schemas in one environment and executing them in others.
B. Manually create the schemas in each environment using the Snowflake web UI, documenting the steps for repeatability.
C. Use a Snowflake client (e.g., SnowSQL) and a script to execute 'CREATE SCHEMA' statements, parameterizing the environment-specific database and schema names.
D. Utilize Snowflake's data replication feature to replicate schemas between different environments
E. Utilize a third-party lac tool (e.g., Terraform) to define the schemas as resources, managing their creation and updates across environments.

正解：E

解説：
Using a dedicated lac tool like Terraform is the best practice for managing Snowflake resources, including schemas. It ensures consistency, repeatability, and version control. Options A and D are manual and error-prone. Option B is better than A and D but lacks the full features of lac tools (state management, dependency resolution, etc.). Option E is for data replication and not schema creation.

質問 # 42
What Snowflake parameter is configured in the Query Processing layer?

A. The minimum and maximum serverless compute limits
B. The sizing of virtual warehouses
C. The types of tables available in an account
D. The minimum and maximum micro-partition limits

正解：B

解説：
The Query Processing layer of Snowflake is wherevirtual warehouses operate, so warehouse sizing parameters (X-Small to 6X-Large) fall under this layer. Warehouse size determines compute power, concurrency, and performance behavior for SQL workloads. Administrators configure warehouse size based on workload intensity, response time requirements, and cost considerations.
Serverless compute limits and micro-partition limits belong to storage and services layers. Table types (permanent, transient, temporary) are storage-level configurations, not part of Query Processing.
Thus, warehouse sizing is the correct parameter configured at the Query Processing layer.

質問 # 43
You are tasked with loading data from an external stage containing gzipped CSV files into a Snowflake table. The CSV files have a header row that you want to skip during the load process.
Additionally, you need to transform the data during the load, specifically concatenating two columns (FIRST NAME and LAST NAME) into a new column called FULL NAME. Which combination of the following COPY INTO options will accomplish this task? Choose all that apply.

A. TRANSFORM AS SELECT $1, $2, $3 II ' II $4 FROM @mystage/data.csv
B. TRANSFORM = (FULL_NAME = FIRST_NAME II ' ' II LAST_NAME)
C. FILE FORMAT = (TYPE = CSV, SKIP_HEADER = 1, COMPRESSION = 'GZIP'), TRANSFORM AS SELECT $1, $2, $3 II " II $4 FROM @mystage/data.csv
D. ON ERROR = 'SKIP FILE'
E. FILE_FORMAT = (TYPE = CSV, SKIP_HEADER = 1 , COMPRESSION = 'GZIP')

正解：A、E

解説：
Option A sets the FILE FORMAT correctly specifying the file type as CSV, skipping the header row (SKIP HEADER = 1), and specifying the compression type as GZIP. Option D correctly transforms the data by selecting all columns and concatenating the columns. This option assumes the file format is already defined in the COPY INTO statement. The @mystage/data.csv after FROM should only include name of the stage name and not the filename. However, that is not part of copy options but part of FROM Statement. The TRANSFORMS parameters that are part of copy options are deprecated.

質問 # 44
What parameter is used to define how long Time Travel can be used to access a table?

A. USE_CACHED_RESULT
B. DATA_RETENTION_TIME_IN_DAYS
C. DATE_OUTPUT_FORMAT
D. TIMEZONE

正解：B

解説：
The DATA_RETENTION_TIME_IN_DAYS parameter controls the Time Travel retention period for Snowflake objects such as tables, schemas, and databases. It specifies the number of days that historical data (prior versions of rows or dropped objects) is retained and accessible via Time Travel. Within this retention window, users can query data "as of" a previous time, restore dropped objects, or clone objects at a historical point.
DATE_OUTPUT_FORMAT determines the display format for date values and is unrelated to historical retention. TIMEZONE affects how timestamps are interpreted and displayed, not how long data history is preserved. USE_CACHED_RESULT governs whether Snowflake may return cached query results, not Time Travel behavior.

質問 # 45
You have enabled auto-ingest using Snowpipe for a stage containing image files. You want to create a Directory Table to track the metadata of these image files (name, size, last modified time). After creating the Directory Table, you notice that it is not automatically updated when new image files are added to the stage. What steps should you take to ensure the Directory Table is automatically updated when new image files are added to the stage?

A. Schedule a task to periodically refresh the Directory Table using the ALTER DIRECTORY TABLE ...
REFRESH command.
B. Set the 'directoryTableAutorefreshEnabled' parameter to TRUE when creating or altering the stage using `CREATE OR ALTER STAGE my_stage DIRECTORY = ( ENABLE = TRUE AUTO_REFRESH = TRUE);'
C. Set the 'directoryTableAutorefreshEnabled' parameter to TRUE at the account level using 'ALTER ACCOUNT SET directoryTableAutorefreshEnabled = TRUE;' and refresh the directory table using
'ALTER DIRECTORY TABLE REFRESH'
D. Configure an event notification service (e.g., AWS SQS) to trigger a Snowpipe load whenever a new file is added to the stage, which implicitly updates the Directory Table.
E. Directory Tables do not automatically refresh. You must always manually refresh them using the ALTER DIRECTORY TABLE command.

正解：C

解説：
To enable automatic refreshing of a Directory Table, you must set the
'directoryTableAutorefreshEnabled' parameter to TRUE at the account level. Also, you must refresh the directory table using 'ALTER DIRECTORY TABLE REFRESH' for the setting to take effect. Option A suggests using a scheduled task, but this is not the automatic way to refresh them. Option B refers to Snowpipe, which is related to data loading, not directly to Directory Table updates. Option D is incorrect as Directory Tables can be set to refresh automatically. Option E sets properties on the stage which is necessary, but not sufficient; the account level setting is also required.

質問 # 46
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2026年MogiExamの最新SOL-C01 PDFダンプおよびSOL-C01試験エンジンの無料共有：https://drive.google.com/open?id=1rYicOKj2P0iDHQgg4UL8TeFwuT3d9su9