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Snowflake SOL-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	Snowflake SnowPro Associate - Platform Certification
Exam Number:	SOL-C01
Related Certifications:	SnowPro Advanced Data Engineer SnowPro Advanced Architect SnowPro Core Certification
Passing Score:	750/1000
Exam Duration:	115 minutes
Exam Format:	Multiple Choice, Multiple Select
Real Exam Qty:	80-100
Certificate Validity Period:	2 years
Exam Price:	$175 USD
Available Languages:	Japanese, English
Recommended Training:	Snowflake Learning & Training Snowflake Documentation
Exam Registration:	Snowflake Certifications Portal
Sample Questions:	Snowflake SOL-C01 Sample Questions
Exam Way:	Online proctored exam via Snowflake certification platform or authorized testing provider.
Pre Condition:	No formal prerequisites required; basic knowledge of data warehousing and SQL recommended.
Official Syllabus URL:	https://www.snowflake.com/en/certifications/

>> SOL-C01완벽한 공부자료 <<

Snowflake SOL-C01 시험요강:

주제	소개
주제 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.
주제 2	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.
주제 3	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.
주제 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.

최신 SnowPro Advanced SOL-C01 무료샘플문제 (Q191-Q196):

질문 # 191
Which of the following is a potential use case for the CLASSIFY_TEXT function?

A. Generating SQL code from natural language description
B. Translating a user manual into multiple languages
C. Categorizing customer support tickets based on topic
D. Extracting data from scanned invoices

정답：C

설명：
CLASSIFY_TEXTis a Snowflake Cortex task-specific function designed to categorize free-form text into predefined buckets. It operates by applying LLM-driven classification logic based on category labels or descriptions provided by the user. A primary and practical use case isautomatically categorizing customer support ticketsbased on content, such as identifying billing issues, login problems, or product defects. This enables automated routing, prioritization, and operational analytics.
The function outputs structured results with the predicted label and confidence score. It is optimized for domain classification rather than open-ended text generation.
Incorrect options:
* Generating SQL codeis handled by the COMPLETE function.
* Translationis handled by TRANSLATE.
* Extracting structured fields from documentsis the role of PARSE_DOCUMENT.
Thus, classification of incoming text streams is the natural use case of CLASSIFY_TEXT.

질문 # 192
A data warehouse contains a table 'SALES TRANSACTIONS that is frequently updated. You need to ensure that the data is protected against accidental deletions and updates, and you need to be able to restore the data to a specific point in time. Which of the following Snowflake features and configurations should you implement to achieve this goal effectively?

A. Set the `DATA RETENTION TIME IN DAYS' parameter at the account level to a sufficient value, and understand that Fail-safe provides an additional layer of protection, although not directly accessible.
B. Enable replication across multiple regions to protect against regional outages and ensure data availability. This is the only protection required.
C. Set the `DATA RETENTION TIME IN DAYS' parameter to a sufficiently long period (e.g., 90 days) and regularly create backups using Snowflake's data export feature.
D. Rely solely on Fail-safe as this guarantees long-term data recovery and provides automated recovery capabilities. Set Data Retention parameter to 1.

정답：A

설명：
Setting the parameter enables Time Travel, which allows restoring data to a specific point in the past within the retention period. Fail-safe provides an additional layer of protection beyond Time Travel, but it's not directly accessible for user- initiated recovery. Replication (Option A) is for disaster recovery but doesn't help with user errors. Backups (Option B) are useful but add complexity compared to Time Travel. Option D relies solely on Fail-safe, which is not user- accessible. Note, it is best to only provide time travel on required databases to save money on storage costs, account level might be unnecessary. Therefore, C is still better option than setting at table level also.

질문 # 193
You are loading data from a set of compressed (gzip) JSON files in an external stage named
'json_stage'. The target table is named raw_event' , and it has a single VARIANT column named
'event_data'. You want to load the data using a virtual warehouse named 'LOAD WH'. You also need to ensure only new files added to the stage since the last load are processed and any pre- existing data is not reloaded. What combination of parameters and setup do you need to use to successfully accomplish this?

A. Use the 'COPY INTO command with the NATCH_BY COLUMN_NAME option set to
'CASE_INSENSITIVE', a warehouse clause, create a pipe using auto- ingest with event notifications, and set the 'AUTO_REFRESH' parameter to 'TRUE on the stage.
B. Use the `COPY INTO command with the FILES = ('json_stage/ option, a warehouse clause, enable the parameter on the stage to incrementally load new files.
C. Use the 'COPY INTO' command with a warehouse clause, enable snowpipe auto-ingest using event notifications and a Snowflake-managed queue, and rely on the metadata history to prevent reprocessing.
D. Use the 'COPY INTO' command with a warehouse clause, specifying the file format to handle gzip compression, and then call the same 'COPY INTO' statement using a scheduled task.
E. Use the 'COPY INTO' command with a warehouse clause, specifying the file format to handle gzip compression, and enable the 'AUTO_REFRESH' parameter to 'TRUE on the stage, and rely on filename to determine which files have been loaded.

정답：C

설명：
Option C is the most appropriate because it leverages Snowpipe's auto-ingest feature using event notifications. Snowpipe automatically tracks which files have already been loaded, preventing reprocessing. Using a virtual warehouse in the 'COPY INTO' statement ensures that the data is loaded with the specified compute resources.

질문 # 194
Consider a scenario where you need to create a custom function (UDF) in Snowflake that uses an external Python library not available in the Snowflake Anaconda channel. How would you package and deploy this UDF, ensuring proper dependency management and version control?

A. Install the required Python libraries directly on the Snowflake compute nodes using a startup script, and then create the UDF referencing the installed libraries.
B. Copy the source code of the external Python library and include it directly within the UDF definition.Snowflake will automatically resolve the dependencies.
C. Package the UDF code and the external library as a single Python file, upload it to a Snowflake stage, and then create the UDF referencing the uploaded file.
D. Create a Python virtual environment, install the necessary libraries, and upload the entire environment as a ZIP file to a Snowflake stage. Then, create the UDF referencing the uploaded ZIP file.
E. Use the `snowflake-snowpark-python' library to create a Snowpark session, package the UDF and its dependencies using Conda, and deploy the resulting environment to a Snowflake stage, referencing it in the UDF definition.

정답：E

설명：
Option D describes the recommended approach using Snowpark and Conda. This allows for proper dependency management and version control by packaging the UDF and its dependencies into a Conda environment. Options A and C lack proper dependency management.
Option B is not feasible as you cannot directly install packages on Snowflake compute nodes.
Option E is impractical and violates code organization principles.

질문 # 195
You are responsible for managing a Snowflake environment where data loading and transformation are performed. You need to monitor the resource consumption of various tasks and identify potential bottlenecks. Which of the following INFORMATION SCHEMA views or functions would be MOST helpful for identifying the warehouse consumption and execution time of individual tasks?

A. ACCOUNT USAGE.WAREHOUSE LOAD HISTORY
B. ACCOUNT USAGE.TASK HISTORY
C. INFORMATION SCHEMA.TABLES
D. DATABASE STORAGE USAGE HISTORY
E. ACCOUNT_USAGE.QUERY HISTORY and join it with ACCOUNT_USAGE.WAREHOUSE EVENTS

정답：B

설명：
The HISTORY view provides detailed information about the execution of tasks, including their start and end times, warehouse used, and status. This allows you to directly analyze the resource consumption of individual tasks. HISTORY is useful for understanding the overall warehouse utilization, but not necessarily specific to tasks. Combining 'QUERY_HISTORY' and
'WAREHOUSE_EVENTS provides broader query level warehouse usage but is more complex than simply using TASK_HISTORY. is about storage not compute, and TABLES just provides table metadata.

질문 # 196
......

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시험패스에유효한SOL-C01완벽한공부자료최신버전덤프데모문제다운

Snowflake SOL-C01 Exam Overview:

최신 SOL-C01시험덤프, SOL-C01시험자료, 최강 SOL-C01 인증시험문제

Snowflake SOL-C01 시험요강:

최신 SnowPro Advanced SOL-C01 무료샘플문제 (Q191-Q196):