2026 Realistic Verified SOL-C01 exam dumps Q&As - SOL-C01 Free Update
Use Real SOL-C01 Dumps - 100% Free SOL-C01 Exam Dumps
NEW QUESTION # 67
How do databases, schemas, tables, and views interact in Snowflake's hierarchy? (Choose any 2 options)
- A. Schemas manage network settings
- B. Tables and views store data and provide virtual tables
- C. A view can contain multiple databases
- D. A database contains schemas, which contain tables and views
Answer: B,D
Explanation:
Snowflake's object hierarchy maintains a structured namespace. At the top is thedatabase, which serves as a container for related schemas. Eachschemaorganizes logical groupings of tables, views, stages, file formats, and other database objects.
Tablesstore persistent, structured data.
Viewsprovide virtual representations of data through stored SQL queries. They do not store data themselves but reference underlying tables.
The hierarchical structure is:
<database>.<schema>.<object>
Incorrect statements:
* Views do not contain databases-they reference objects but do not encapsulate them.
* Schemas do not manage network or cloud settings; those are defined at account or cloud service layers.
This hierarchy enables clear object governance, secure sharing, and proper namespace organization.
NEW QUESTION # 68
You are developing a Snowflake Notebook to analyze sales data. You want to create a dynamic SQL query that filters data based on a parameter passed from a user interface element (e.g., a dropdown). How can you best achieve this within a Snowflake Notebook, ensuring SQL injection vulnerabilities are mitigated?
- A. Use Snowflake's built-in parameterization feature within the SQL cell. For example: ```sql SELECT FROM sales WHERE region = :region_param; and then assign the `region_param' variable in a Python cell.
- B. Use the `snowflake.connector' library to create a prepared statement with parameterized query execution from a Python cell.
- C. Directly concatenate the parameter value into the SQL query string using Python string formatting.
- D. Use JavaScript stored procedure to generate SQL string and execute the query.
- E. Store the parameter in a Snowflake session variable and reference the session variable in the SQL query string.
Answer: A,B
Explanation:
Options B and C are the most secure and recommended approaches. Option B leverages Snowflake Notebook's built-in parameterization, which handles escaping and prevents SQL injection. Option C uses the `snowflake.connector' to achieve similar result. Option A is highly susceptible to SQL injection. Option D is less suitable for dynamic parameters from a I-Jl. Option E increases the complexity significantly and doesn't directly address parameterization within the notebook environment.
NEW QUESTION # 69
What tasks can be performed using Snowflake Cortex AI? (Select TWO).
- A. Share data through the Snowflake Marketplace.
- B. Extract and classify text.
- C. Enhanced data security.
- D. Simplify unstructured data workflows.
- E. Load semi-structured data.
Answer: B,D
Explanation:
Snowflake Cortex AI provides built-in AI functions and tools designed to work natively with unstructured and structured data. Two key capabilities are:
* Extract and classify text using functions like PARSE_DOCUMENT, EXTRACT_TEXT, and classification models. Cortex can process documents, identify relevant fields, and convert unstructured content into usable structured formats.
* Simplify unstructured data workflows by combining document extraction, vector search, summarization, and AI reasoning tools (e.g., Cortex Analyst, Cortex Search) directly inside Snowflake without external services.
It does not provide Marketplace data sharing features, which belong to Snowflake's Data Sharing platform.
Loading semi-structured data is a core Snowflake capability using VARIANT and COPY INTO-not Cortex- specific. Enhancing data security is a platform-wide feature, not a Cortex function.
NEW QUESTION # 70
A data engineer is using Snowflake Notebooks to perform exploratory data analysis on a large dataset stored in a Snowflake table called 'ORDERS. They want to calculate the total order amount per day and visualize the results as a line chart directly within the notebook. Which sequence of actions represents the MOST efficient and correct way to achieve this?
- A. Write a Javascript function within the Snowflake Notebook to fetch the data using fetch API and calculate the total order amount per day. Then, use a JavaScript charting library like Chart.js to generate the line chart.
- B. Export the 'ORDERS' table as a CSV file, load it into a separate Python environment, perform the calculations using Pandas, and then use a plotting library to generate the chart.
- C. Write a SQL query within the notebook to calculate the total order amount per day, store the result in a Pandas DataFrame, and use a plotting library like Matplotlib or Plotly to generate the line chart within the notebook.
- D. Create a stored procedure in Snowflake that calculates the total order amount per day and returns the result as a table, then call the stored procedure from the notebook and use a plotting library to visualize the data.
- E. Use the SnowflakeUIto create a view that calculates the total order amount per day, then import the view into the Notebook as a Dataframe and then use a plotting library to generate the chart.
Then use a plotting library like Matplotlib or Plotly to generate the line chart within the notebook.
Answer: C
Explanation:
The most efficient approach is to directly query Snowflake from the notebook, use Pandas for data manipulation, and Matplotlib or Plotly for visualization. This leverages the compute power of Snowflake and avoids unnecessary data transfer. Exporting to CSV (B) is inefficient, Javascript (D) isn't the typical notebook flow and stored procedures adds extra complexity (E). Creating a view and importing it (C) adds an extra step compared to directly querying.
NEW QUESTION # 71
You have created a virtual warehouse in Snowflake and loaded data into several tables. You observe that query performance is inconsistent, with some queries running quickly and others taking significantly longer, even though they access similar data. You suspect resource contention is the issue. What steps can you take to improve query performance and manage resource contention effectively? Select all that apply.
- A. Implement workload management rules to prioritize critical queries and limit resource consumption for less important tasks.
- B. Increase the size of the virtual warehouse to provide more computing resources.
- C. Create separate virtual warehouses for different workloads (e.g., data loading, reporting) to isolate resource usage.
- D. Enable query acceleration to offload suitable workloads and reduce warehouse load.
- E. Reduce the number of concurrent users accessing the system.
Answer: A,B,C,D
Explanation:
Options A, B, C, and D are all valid strategies for improving query performance and managing resource contention. Increasing warehouse size (A) provides more resources. Query Acceleration Service(B) can significantly reduce latency for eligible queries and reduce overall warehouse load. Separate warehouses (C) isolate workloads. Workload management (D) allows prioritizing critical queries and limiting resources for others. Option E, while it might alleviate contention, is not a scalable solution and limits usability; better resource management is preferred.
NEW QUESTION # 72
What file formats can be loaded into Snowflake using the COPY INTO command? (Choose any 3 options)
- A. Parquet
- B. JSON
- C. CSV
- D. XLS
Answer: A,B,C
Explanation:
TheCOPY INTOcommand supports loading structured and semi-structured data from various Snowflake stages. Supported file formats includeCSV,JSON,Parquet,Avro,ORC, and others. Each format integrates with Snowflake's file format definitions, enabling configuration for delimiters, compression, headers, and parsing rules.
* CSVis widely used for tabular datasets and supports flexible parsing via FILE_FORMAT options such as FIELD_DELIMITER, SKIP_HEADER, and NULL_IF.
* JSONis loaded natively into VARIANT columns, allowing nested structures to be queried immediately through path notation.
* Parquet, a high-efficiency columnar format, is optimized for analytic workloads and provides excellent compression and schema evolution benefits.
XLS is not supported, as Snowflake does not support Excel spreadsheets directly. XLS/XLSX files must be converted to CSV or Parquet before loading.
The COPY INTO architecture ensures performant ingestion across multi-node compute clusters.
NEW QUESTION # 73
You need to programmatically retrieve query history metadata (e.g., start time, end time, status, user) for queries executed in the last 24 hours using SQL from within Snowsight. However, you only want to retrieve records for queries that took longer than 5 seconds to execute, filtering by a specific user 'DATA USER'. Which of the following SQL statements, when executed in Snowsight, is the MOST efficient way to achieve this?
- A.
- B.
- C.
- D.
- E.
Answer: E
Explanation:
In the view is measured in milliseconds. Therefore, to filter for queries that took longer than 5 seconds, you need to use > 5000'. Options A and D are incorrect because `execution_status' and execution_time' are not valid columns. Option C calculates the difference manually, which, although functional, is less efficient than using the pre- calculated column. Option E uses an interval which is valid, but less performant and harder to read than the numerical comparision from option B. can be directly compared with the integer value representing miliseconds.
NEW QUESTION # 74
Which of the following file formats can be processed by the PARSE_DOCUMENT function?
- A. PPTX
- B. DOCX
- C. PDF
- D. XLSX
Answer: A,B,C
Explanation:
PARSE_DOCUMENT extracts text and, depending on mode, layout information from supported document formats. The function currently supportsPDF,PPTX, andDOCXfiles. For PDFs, it can perform OCR to extract scanned text or use LAYOUT mode to detect tables and structured regions. For PPTX files, it extracts slide text and content. For DOCX files, the function captures text and applies layout analysis. XLSX files are not supported-Excel content requires different extraction methods. PARSE_DOCUMENT enables automated document intelligence workflows entirely inside Snowflake, eliminating the need for external OCR or parsing services.
NEW QUESTION # 75
How can Python variable substitution be performed in Snowflake notebooks?
- A. By manually editing data files
- B. By writing HTML code
- C. By configuring network settings
- D. By using placeholders in SQL queries
Answer: D
Explanation:
In Snowflake Notebooks, Python and SQL operate in an integrated environment. Python variable substitution is performed usingstring placeholders or f-string interpolationwithin SQL statements. This allows dynamic query construction where Python values are embedded directly into SQL code executed through the Snowpark session.
Example:
table_name = "CUSTOMERS"
session.sql(f"SELECT * FROM {table_name}").show()
This method enables parameterized, context-aware SQL generation-ideal for iterative development, automation, and pipeline construction. It avoids manual text editing and provides consistency across Python and SQL execution layers.
Incorrect responses:
* Network settings and HTML are unrelated to variable handling.
* Editing data files does not apply to dynamic query parameterization.
Variable substitution is essential for combining logic, iteration, and conditional flows within notebooks.
NEW QUESTION # 76
How do you specify a custom delimiter for a CSV file when using COPY INTO <TABLE>?
- A. Using the COLUMN_SEPARATOR query parameter
- B. Using the FIELD_SEPARATOR stage property.
- C. Using the DELIMITER_CHAR table property.
- D. Using the FIELD_DELIMITER file format option
Answer: D
Explanation:
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.
NEW QUESTION # 77
Which SQL command is used to create a new database in Snowflake?
- A. CREATE DATABASE database_name;
- B. USE DB database_name;
- C. USE DATABASE database_name;
- D. CREATE DB database_name;
Answer: A
Explanation:
The correct SQL command for creating a new database in Snowflake isCREATE DATABASE database_name;. This initializes a new logical container for schemas and objects. USE DATABASE switches the active database context but does not create one. Snowflake does not support the shorthand CREATE DB or USE DB syntax. Therefore, CREATE DATABASE is the correct and only valid command for creating a database.
NEW QUESTION # 78
You are designing a Snowflake UDF to parse log files. The UDF needs to accept the log file content as a string argument and a configuration parameter that specifies the log format. Which of the following approaches is MOST efficient and allows for easy updates to the log format without redeploying the UDF?
- A. Store the log format in a named stage and create a function to retrieve the format from the stage using 'GET_OBJECT.
- B. Store the log format in a Snowflake secret and retrieve it using the SYSTEM$GET SECRET function, passing the secret name as an argument to the UDF.
- C. Pass the log format as a string argument directly to the UDF. Parse the string within the UDF to extract the format definition.
- D. Store the log format in a separate table and use a Snowflake lookup within the UDF to retrieve the format based on a key passed as an argument.
- E. Use a stage to store the log format definition as a JSON file. Read the file content from the stage within the UDF using the 'EXTERNAL _ TABLE function.
Answer: B
Explanation:
Option E is the best solution. Storing the log format in a Snowflake secret offers the most secure and manageable approach. Secrets are designed to store sensitive configuration data, and SYSTEM$GET_SECRET ensures that the UDF can access the format without embedding it directly in the code. This allows for updates without redeploying the UDF. Options A, B, C, and D have drawbacks. Passing as a string (A) is inefficient and less secure. Table lookup (B) adds overhead. External tables/stages (C and D) are more complex and might not be necessary for simple configuration data.
NEW QUESTION # 79
When using the TRANSLATE function, what type of input is expected for the source and target languages?
- A. File paths to language dictionaries
- B. Numerical language identifiers
- C. Full language names (e.g., "English", "French")
- D. Standard language codes (e.g., "en", "fr")
Answer: D
Explanation:
The TRANSLATE function in Snowflake Cortex expects ISO-standard language codes such as "en" for English or "fr" for French. These are two-letter or multi-letter codes defined by international standards.
Snowflake uses these codes to instruct its translation model which languages to interpret and produce. If the source language is unknown, users can pass an empty string to trigger automatic language detection. Full- language names, dictionary file paths, or numeric identifiers are not required or supported. TRANSLATE operates entirely with text input and language codes, ensuring easy integration into SQL workflows for multi- language data pipelines, localization tasks, and multilingual analytics.
NEW QUESTION # 80
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 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
- C. Ensure external functions are enabled at the account level: ALTER ACCOUNT SET ENABLE EXTERNAL FUNCTIONS-TRUE;
- D. Grant the EXECUTE TASK privilege on the account to the role executing the query using. `GRANT EXECUTE TASK ON ACCOUNT TO ROLE
- E. Grant OWNERSHIP on the SNOWFLAKE.ML.COMPLETE to the role executing the query using:GRANT OWNERSHIP ON FUNCTION SNOWFLAKE .ML. COMPLETE TO ROLE;
Answer: A,B,C
Explanation:
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
NEW QUESTION # 81
Which layer of Snowflake's architecture provides security features like encryption and authentication?
- A. Data Storage Layer
- B. Query Processing Layer
- C. Cloud Services Layer
- D. Compute Layer
Answer: C
Explanation:
TheCloud Services Layeris responsible for Snowflake's global security, coordination, and metadata management. It orchestrates all authentication and authorization processes, including username/password login, MFA, OAuth, SSO, key-pair authentication, and integration with enterprise identity providers. It also manages role-based access control (RBAC), privilege enforcement, session management, and secure query orchestration.
This layer centralizes encryption policy enforcement, including the management of Snowflake-managed and customer-managed encryption keys (Tri-Secret Secure). While all data stored and transmitted in Snowflake is encrypted by default, it is the Cloud Services Layer that handles the key lifecycle, rotation, and hierarchy.
The Compute Layer only runs queries.
The Storage Layer keeps compressed, encrypted columnar data but does not manage security controls.
The Query Processing Layer executes SQL but does not manage authentication or RBAC.
Thus, the Cloud Services Layer is the authoritative control plane for Snowflake security.
NEW QUESTION # 82
You are inserting data into a table named 'EVENTS' which contains a column 'EVENT DATA' of type 'VARIANT'. The data being inserted comes from an external stage and is in JSON format.
However, you are encountering errors because some of the JSON documents contain deeply nested arrays and objects, leading to excessive memory consumption during the INSERT operation. Which of the following actions can you take to mitigate this issue and successfully load the data using the 'INSERT' command with data from a stage? Choose TWO.
- A. Break down the INSERT operation into smaller batches, processing subsets of the data at a time.
- B. Use the `VALIDATE function to identify problematic JSON documents and exclude them from the INSERT operation.
- C. Increase the size of the virtual warehouse to provide more memory for the INSERT operation.
- D. Pre-process the JSON data outside of Snowflake to flatten or simplify the nested structures before loading it into the stage.
- E. Use = TRUE file format option for the stage.
Answer: A,D
Explanation:
Options B and E are the most effective solutions. Option B, pre-processing the data to flatten it reduces the complexity of individual JSON documents, lowering memory consumption during the insert. Option E, batching the INSERT operations limits the amount of memory used in each individual operation, preventing the warehouse from being ovemhelmed. Option A, increasing the warehouse size can help, but it's often a more expensive solution than optimizing the data loading process itself. Option C only strips the outer array. Option D does not resolve the issue during load.
NEW QUESTION # 83
You have a Snowflake virtual warehouse named 'COMPUTE that is experiencing performance issues during peak hours. The workload consists of a mix of complex analytical queries and high- volume data loading operations. To optimize performance, you want to implement resource monitoring and auto- scaling. Which of the following strategies would be MOST effective?
- A. Create a resource monitor that triggers notifications when the warehouse's credit consumption exceeds a certain threshold.
- B. Implement workload management rules to prioritize analytical queries over data loading operations.
- C. Increase the size of 'COMPUTE_WH' to a larger T-shirt size (e.g., from Medium to Large).
- D. Create separate virtual warehouses for analytical queries and data loading, configure auto-scaling on each warehouse, and use resource monitors to control credit consumption.
- E. Enable auto-suspend on "COMPUTE_WH' to minimize costs during idle periods.
Answer: D
Explanation:
Option D provides the most effective solution. Separating workloads onto dedicated warehouses allows for independent scaling and optimization. Auto-scaling ensures that each warehouse can adjust its resources dynamically to meet the demands of its specific workload. Resource monitors help control costs and prevent runaway credit consumption. While increasing warehouse size (A) might improve performance, it's not as targeted as workload separation. Resource monitors and auto-suspend (B and C) are helpful but don't address the core issue of workload contention.
Workload management rules (E) can help prioritize, but don't scale resources dynamically.
NEW QUESTION # 84
Which command will create a named internal stage?
- A. CREATE STAGE
- B. CREATE NAMED STAGE
- C. CREATE INTERNAL STAGE
- D. CREATE @STAGE
Answer: A
Explanation:
CREATE STAGE is the correct Snowflake DDL command for creating a named internal stage. Stages hold files for loading/unloading. The command defaults to internal storage unless external parameters are provided.
Other options are not valid Snowflake syntax.
NEW QUESTION # 85
What kind of information can be found in Query History in Snowsight? (Select THREE)
- A. Execution Status (e.g. Succeeded, Failed)
- B. Credit consumption
- C. Query ID
- D. Virtual Warehouse size used
- E. Duration of the query
Answer: A,C,E
Explanation:
Query History in Snowsight includes:
* Query ID - A unique identifier for each executed query.
* Execution Status - Indicates whether the query succeeded, failed, or was canceled.
* Duration - Total time taken for the query to run.
While warehouse name is shown,warehouse sizeis not explicitly displayed. Credit consumption appears in Account Usage views, not directly in Query History.
NEW QUESTION # 86
What Snowflake object provides a secure connection to external cloud storage?
- A. A named file format
- B. An external stage
- C. An external table
- D. A directory table
Answer: B
Explanation:
An external stage is the Snowflake object that encapsulates a secure connection to external cloud storage such as Amazon S3, Azure Blob Storage, or Google Cloud Storage. It stores the location (URL or bucket path) and, where required, credentials or role-based access configuration, and may also reference a file format. External stages are used as the source or target for COPY INTO operations when loading from or unloading to external storage.
An external table provides a logical SQL interface to data stored externally but relies on a stage for connectivity; it does not itself define the connection. A directory table exposes metadata about files stored in a stage, not the connection. A named file format defines parsing rules (type, delimiter, compression) but has no knowledge of or connection to a specific external storage location.
NEW QUESTION # 87
Which of the following statements about creating databases and schemas in Snowflake are TRUE? (Select TWO)
- A. A database can contain multiple schemas, each serving as a logical grouping of database objects.
- B. A database must be created before any schemas can be created within it.
- C. A schema can be created without specifying a database if the current session's context is set to a specific database.
- D. Schemas are optional and not required to organize objects within a database.
- E. The 'CREATE OR REPLACE DATABASE command allows you to change the edition of Snowflake being used.
Answer: A,B
Explanation:
Option A is correct because a database serves as the top-level container for all other objects, including schemas. Option E is correct because schemas are used to logically organize database objects. Option B is incorrect, though schemas can be created without specifying a database when the database is specified in context. Option C is incorrect because 'CREATE OR REPLACE DATABASE is for replacing existing databases, not changing the Snowflake edition. Option D is incorrect because schemas are required to organize objects.
NEW QUESTION # 88
You are tasked with loading data from a series of CSV files stored in an Amazon S3 bucket into Snowflake. The CSV files contain a header row, but some files have slight variations in the number and order of columns. You want to ensure that all relevant data is loaded correctly, even if the column order differs, and that any extra columns are ignored. Which of the following approaches is the MOST appropriate and efficient?
- A. Pre-process the CSV files to standardize the column order and names before loading them into Snowflake.
- B. Create a VIEW on top of the external table to ensure that column names are consistent across all files.
Then load the data into view. - C. Define a single external table with a VARIANT column and use Snowflake's CSV parsing capabilities to load all files into that column. Then, extract the relevant data using JSON path expressions.
- D. Create a single target table with all possible columns from all CSV files, using 'SKIP_HEADER = 1' and explicitly map the columns in the 'COPY INTO' statement to the correct columns in the target table, using the 'FILE FORMAT option to specify the correct field delimiter.
- E. Create a separate external table for each CSV file with a different column structure.
Answer: D
Explanation:
Creating a single target table with all possible columns and explicitly mapping the columns in the
'COPY INTO' statement is the most appropriate. This approach handles variations in column order by explicitly mapping columns from the CSV files to the target table. It is more performant than VARIANT, and doesn't require external preprocessing. Option A is not scalable and difficult to maintain. Option B is suitable for schema evolution but is not recommended if schemas are already known. Option D loading data into view, is not direct approach, and requires external table and COPY command need a table not view to load. Option E pre-processing helps if data consistency is high priority, but adds complexity to workflow and is not part of Snowflake functionalities.
NEW QUESTION # 89
You are using PARSE DOCUMENT on documents stored in an external stage. You notice that some documents are skipped during processing without any explicit error messages. Which of the following could be the MOST likely reason(s) for this behavior, and how would you address it?
- A. The documents do not contain any text. Verify that the documents actually contain readable text and are not just images or empty files. Remove the files without text
- B. Snowflake encountered an internal error during processing and silently skipped the document.
Review the Snowflake query history for error messages or contact Snowflake support for assistance. - C. The virtual warehouse is being automatically suspended due to inactivity. Configure auto-resume for the virtual warehouse to ensure it remains active during processing. Also, explicitly specify the virtual warehouse to use by using USE WAREHOUSE
- D. The documents are too large. Increase the virtual warehouse size and set the MAX FILE SIZE parameter in the file format to a higher value (e.g., 16MB), and retry the operation.
- E. The documents are in an unsupported format. Ensure that the documents are in a supported format (PDF, DOCX, etc.) and that Snowflake has the necessary permissions to access the external stage. Check the 'VALIDATE function to verify if the data file is valid before parsing.
Answer: C,E
Explanation:
Option B is a common cause, where unsupported format and permissions are key things to consider and `VALIDATE' helps ensure data is valid. Option C is also correct, because virtual warehouse suspension can interrupt processing and specifying the warehouse ensures it's used.
Option A, while relevant to performance, doesn't explain silent skipping unless the files grossly exceed limits. Option D is possible but less likely than B or C and harder to diagnose without specific error messages. Option E is less likely, because even if the content is not parseable, it still doesn't explain why document is skipped silently.
NEW QUESTION # 90
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.TASK HISTORY
- B. ACCOUNT USAGE.WAREHOUSE LOAD HISTORY
- C. ACCOUNT_USAGE.QUERY HISTORY and join it with ACCOUNT_USAGE.WAREHOUSE EVENTS
- D. DATABASE STORAGE USAGE HISTORY
- E. INFORMATION SCHEMA.TABLES
Answer: A
Explanation:
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.
NEW QUESTION # 91
A team is using Snowsight for developing and testing SQL queries. They want to ensure that all queries executed by a specific user, analystl', are automatically tagged with a custom tag
'team:analyticS. Which of the following methods, leveraging Snowsight or Snowflake features, achieves this goal with minimal administrative overhead? Select all that apply.
- A. Use Snowsight
team:analytics at the start of every worksheet. - B. There is no method to enforce automatic tagging of queries in Snowsight or Snowflake without manually adding the tag to each query.
- C. Create a Snowflake resource monitor that automatically applies the `team:analytics' tag to all queries executed by `analystl ` .
s code auto-completion to auto-append ALTER SESSION SET QUERY_TAG = - D. Set the QUERY_TAG parameter at the user level using 'ALTER USER analystl SET QUERY_TAG
= 'team:analytics';'. - E. Configure the QUERY _ TAG session parameter for each Snowsight worksheet used by `analystl
Answer: D
Explanation:
Setting the QUERY TAG parameter at the user level (Option D) is the most efficient and centralized way to ensure all queries executed by `analystl are automatically tagged. This avoids the need for individual worksheet configuration. Option A requires manual configuration for each worksheet, which is not ideal. Option B Resource Monitors do not have tagging capabilities.
Option C also requires manual updates to each worksheet. Option E is incorrect, as user-level parameter setting provides the desired functionality.
NEW QUESTION # 92
......
Snowflake SOL-C01 Exam Syllabus Topics:
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
| Topic 4 |
|
Pass SOL-C01 exam Updated 218 Questions: https://testking.guidetorrent.com/SOL-C01-dumps-questions.html