SnowPro-Core Exam Dumps - SnowPro Core Certification Exam

The CURRENT_TASK_GRAPHS table function is designed to provide information on Directed Acyclic Graphs (DAGs) that are currently scheduled or executing within Snowflake. This function offers insights into the structure and status of task chains, enabling users to monitor and troubleshoot task executions. DAGs in Snowflake represent sequences of tasks with dependencies, and understanding their current state is crucial for managing complex workflows.References: Snowflake Documentation on Task Management

Access History in Snowflake is primarily utilized for compliance auditing. The Access History feature provides detailed logs that track data access and modifications, including queries that read from or write to database objects. This information is crucial for organizations to meet regulatory requirements and to perform audits related to data access and usage.

• Role of Access History: Access History logs are designed to help organizations understand who accessed what data and when. This is particularly important for compliance with various regulations that require detailed auditing capabilities.
• How Access History Supports Compliance Auditing:

The REMOVE command is used in Snowflake to delete files from a stage after they have been successfully ingested into Snowflake tables. This command helps manage storage by allowing users to clean up staged files that are no longer needed, ensuring that the stage does not accumulate unnecessary data over time. Unlike DELETE, DROP, or TRUNCATE commands, which are used for managing data within Snowflake tables or dropping objects, REMOVE specifically targets the management of files in stages.References: Snowflake Documentation on Stages and File Management

Parquet is a columnar storage file format that is optimized for efficiency in both storage and processing. It supports compression and encoding schemes that significantly reduce the storage space needed and speed up data retrieval operations, making it ideal for handling large volumes of data. Unlike JSON or TSV, which are row-oriented and typically uncompressed, Parquet is designed specifically for use with big data frameworks, offering advantages in terms of performance and cost when storing and querying semi-structured data.References: Apache Parquet Documentation

In Snowflake, a Share is the object that contains all the information required to share a database with other Snowflake accounts. Shares are used to securely share data stored in Snowflake tables and views, enabling data providers to grant data consumers access to their datasets without duplicating data. When a database is shared, it can include one or more schemas, and each schema can contain tables, views, or both.

References:

• Snowflake Documentation on Shares: Shares

Snowflake's Continuous Data Protection (CDP) features encompass several mechanisms designed to protect data and ensure its availability and recoverability. Among these features, the one that has associated costs is Fail-safe.

Fail-safe is an additional layer of protection that kicks in after the Time Travel period expires. While Time Travel allows users to access historical data within a defined retention period (which can vary from 1 to 90 days depending on the Snowflake edition), Fail-safe provides a further 7 days (for a total of 7 additional days beyond the Time Travel period) during which Snowflake retains the data. This period is primarily intended for Snowflake's internal operations to recover data in the event of extreme scenarios, such as significant operational failures, and is not directly accessible by customers for data recovery purposes.

The associated costs with Fail-safe arise because Snowflake continues to store the data beyond the customer-specified Time Travel period, thereby incurring additional storage costs. It's important to note that while users do not incur direct costs for enabling Fail-safe (as it is an automatic feature of Snowflake), the extended storage of data during this period contributes to overall storage costs.

References:

• Snowflake Documentation on Continuous Data Protection: Continuous Data Protection (CDP)
• Snowflake Documentation on Fail-safe: Understanding Fail-safe

When using the COPY INTO <</b>table> command to load CSV data from a stage into a Snowflake table, one of the crucial guidelines to follow is that the number of columns in each row within the CSV file should be consistent. This ensures data integrity and allows for a smooth data loading process, as Snowflake expects each row in the CSV file to map directly to a row in the target table based on the number of columns. If there's a mismatch in the number of columns between any row in the file and the target table, Snowflake might return an error or produce unexpected results during the load operation.

It's important to note that while the CSV field delimiter can be specified to something other than a comma, ensuring the consistency in the number of columns across all rows is fundamental to successfully loading data.

References:

• Snowflake Documentation on Loading Data: Loading CSV Data