A financial services company needs to isolate sensitive production data from development data within the same region and support secure data transfer between environments. What is the best solution?
Correct Answer: A
Strong isolation of sensitive production data from development environments is best achieved using separate Snowflake accounts. Account-level isolation ensures independent security boundaries, network policies, and governance controls. Secure Data Sharing allows controlled, read-only access to production data without copying it, supporting safe data transfer between environments (Answer A). Using cloning across accounts is not supported; cloning works only within the same account. Database-level separation does not provide the same security guarantees as account-level isolation, and user-level network policies are not supported in Snowflake. This design aligns with SnowPro Architect best practices for regulated industries, emphasizing strong isolation, least privilege, and secure data access mechanisms.
Question 197
A retailer's enterprise data organization is exploring the use of Data Vault 2.0 to model its data lake solution. A Snowflake Architect has been asked to provide recommendations for using Data Vault 2.0 on Snowflake. What should the Architect tell the data organization? (Select TWO).
Correct Answer: A,C
Data Vault 2.0 on Snowflake supports the HASH_DIFF concept for change data capture, which is a method to detect changes in the data by comparing the hash values of the records. Additionally, Snowflake's multi-table insert feature allows for the loading of multiple PIT tables in parallel from a single join query, which can significantly streamline the data loading process and improve performance1. References = *Snowflake's documentation on multi-table inserts1 *Blog post on optimizing Data Vault architecture on Snowflake2
Question 198
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. What could be causing this?
Correct Answer: B
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. This could be caused by the following factors: * The order of the keys in the JSON was changed. Snowflake stores semi-structured data internally in a column-like structure for the most common elements, and the remainder in a leftovers-like column. The order of the keys in the JSON affects how Snowflake determines the common elements and how it optimizes the query performance. If the order of the keys in the JSON was changed, Snowflake might have to re-parse the data and re-organize the internal storage, which could result in slower query performance. * There were variations in string lengths for the JSON values in the recent data imports. Non-native values, such as dates and timestamps, are stored as strings when loaded into a VARIANT column. Operations on these values could be slower and also consume more space than when stored in a relational column with the corresponding data type. If there were variations in string lengths for the JSON values in the recent data imports, Snowflake might have to allocate more space and perform more conversions, which could also result in slower query performance. The other options are not valid causes for poor query performance: * There were JSON nulls in the recent data imports. Snowflake supports two types of null values in semi- structured data: SQL NULL and JSON null. SQL NULL means the value is missing or unknown, while JSON null means the value is explicitly set to null. Snowflake can distinguish between these two types of null values and handle them accordingly. Having JSON nulls in the recent data imports should not affect the query performance significantly. * The recent data imports contained fewer fields than usual. Snowflake can handle semi-structured data with varying schemas and fields. Having fewer fields than usual in the recent data imports should not affect the query performance significantly, as Snowflake can still optimize the data ingestion and query execution based on the existing fields. Considerations for Semi-structured Data Stored in VARIANT Snowflake Architect Training Snowflake query performance on unique element in variant column Snowflake variant performance
Question 199
Which columns can be included in an external table schema? (Select THREE).
Correct Answer: A,C,D
Question 200
When activating Tri-Secret Secure in a hierarchical encryption model in a Snowflake account, at what level is the customer-managed key used?
Correct Answer: B
Tri-Secret Secure is a feature that allows customers to use their own key, called the customer-managed key (CMK), in addition to the Snowflake-managed key, to create a composite master key that encrypts the data in Snowflake. The composite master key is also known as the account master key (AMK), as it is unique for each account and encrypts the table master keys (TMKs) that encrypt the file keys that encrypt the data files. The customer-managed key is used at the account level, not at the root level, the table level, or the micro- partition level. The root level is protected by a hardware security module (HSM), the table level is protected by the TMKs, and the micro-partition level is protected by the file keys12. References: Understanding Encryption Key Management in Snowflake Tri-Secret Secure FAQ for Snowflake on AWS
