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| Section | Objectives |
|---|---|
| Topic 1: Data Loading and Unloading | - External and internal stages - Bulk data loading using stages and COPY INTO |
| Topic 2: Data Sharing and Protection | - Data replication and failover concepts - Secure data sharing |
| Topic 3: Snowflake Architecture and Platform Concepts | - Virtual warehouses and compute model - Snowflake architecture fundamentals - Storage and data organization |
| Topic 4: Performance and Optimization | - Warehouse sizing and scaling - Query optimization basics |
| Topic 5: Security and Access Control | - Role-based access control (RBAC) - Authentication and user management |
| Topic 6: Data Transformation and Processing | - SQL operations in Snowflake - Streams and tasks for data pipelines |
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NEW QUESTION # 95
At what level is the MIN_DATA_RETENTION_TIME_IN_DAYS parameter set?
Answer: D
Explanation:
The MIN_DATA_RETENTION_TIME_IN_DAYS parameter is set at the account level.This parameter determines the minimum number of days Snowflake retains historical data for Time Travel operations
NEW QUESTION # 96
Why does Snowflake recommend file sizes of 100-250 MB compressed when loading data?
Answer: C
Explanation:
Snowflake recommends file sizes between 100-250 MB compressed when loading data to optimize parallel processing.Smaller, compressed files can be loaded in parallel, which maximizes the efficiency of the virtual warehouses and speeds up the data loading process
NEW QUESTION # 97
A user needs to connect a private GitHub repository to Snowflake for stored-procedure version control. The user's organization requires secure authentication.
Which authentication method should be configured for access to the private GitHub repository?
Answer: B
Explanation:
For a private GitHub repository accessed over HTTPS, Snowflake can authenticate using a GitHub personal access token. The token is stored securely in a Snowflake secret rather than written directly into the Git repository object.
The secret is referenced by the API integration or Git repository configuration. For GitHub, a personal access token is generally supplied in the password field of a password-type Snowflake secret.
A normal GitHub account password is not the recommended authentication mechanism for Git operations.
Interactive MFA cannot be performed by Snowflake when it synchronizes a repository. An arbitrary OAuth token is not the standard answer for the documented private-GitHub setup represented by this question.
References:
Snowflake Documentation - Setting up Snowflake to use Git: https://docs.snowflake.com/en/developer-guide
/git/git-setting-up
Snowflake Documentation - Using a Git repository in Snowflake: https://docs.snowflake.com/en/developer- guide/git/git-overview
==
NEW QUESTION # 98
What does Snowflake recommend as a best practice for using secure views?
Answer: B
Explanation:
Snowflake recommends not exposing sequence-generated columns in secure views. Secure views are used to protect sensitive data by ensuring that users can only access data for which they have permissions. Exposing sequence-generated columns can potentially reveal information about the underlying data structure or the number of rows, which might be sensitive.
Create Secure Views: Define secure views using the SECURE keyword to ensure they comply with Snowflake's security policies.
Exclude Sensitive Columns: When creating secure views, exclude columns that might expose sensitive information, such as sequence-generated columns.
CREATE SECURE VIEW secure_view AS
SELECT col1, col2
FROM sensitive_table
WHERE sensitive_column IS NOT NULL;
References:
Snowflake Documentation: Secure Views
Snowflake Documentation: Creating Secure Views
These answers and explanations should provide comprehensive guidance on the specified Snowflake topics.
NEW QUESTION # 99
What best practice recommendations will help prevent timeouts when using the PUT command to load large data sets? (Select TWO).
Answer: A,D
Explanation:
To avoid timeouts during large data uploads with the PUT command in Snowflake, it is recommended to:
Compress files before loading: Compressed files are smaller and upload faster, reducing the risk of timeouts.
Increase the PARALLEL option value: This option allows more simultaneous upload threads, improving upload speed and efficiency for large datasets.
Semi-structured file formats and table staging do not directly impact timeouts, while enabling overwrite does not prevent timeouts but rather controls overwriting of existing files.
NEW QUESTION # 100
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