For global enterprises handling sensitive data, Snowflake architects must design for both secure access control and secure network connectivity. Network policies allow administrators to restrict access to Snowflake accounts based on approved IP address ranges, ensuring that only corporate offices or trusted networks can connect (Answer D). This is a core Snowflake security control and is frequently tested in the SnowPro Architect exam.
AWS PrivateLink provides private, non-internet-based connectivity between Snowflake and customer AWS VPCs (Answer E). This ensures that data traffic does not traverse the public internet, which is critical for meeting regional regulatory and compliance requirements related to data privacy and sovereignty. PrivateLink also simplifies security posture by reducing exposure to public endpoints.
While SAML authentication is important for identity management, it does not address secure data transport. Public endpoints with SSL are secure but do not meet stricter regulatory requirements when private connectivity is mandated. Snowflake does not provide detailed packet-level network traffic monitoring. Together, network policies and PrivateLink provide strong, compliant, and regionally secure Snowflake architectures.
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QUESTION NO: 17 [Cost Control and Resource Management]
An Architect plans to stream data using the Snowflake Connector for Kafka in Snowpipe.
What setting will optimize costs?
A.
Set buffer.flush.time = 1.
B.
Set buffer.count.records = 1.
C.
Set buffer.size.bytes = 10 M
B.
D.
Maximize the number of micro-partitions.
Answer: C
When using the Snowflake Connector for Kafka with Snowpipe, cost efficiency depends on batching data into appropriately sized files before ingestion. Snowflake charges Snowpipe costs based on the number of files ingested and compute used for loading. Very small files significantly increase overhead and cost.
Setting buffer.size.bytes = 10 MB allows the connector to batch records into reasonably sized files before flushing them to Snowflake (Answer C). This strikes a balance between ingestion latency and cost efficiency and aligns with Snowflake best practices for streaming ingestion. Extremely small buffer sizes or very frequent flush intervals (such as 1 record or 1 second) lead to excessive file creation and higher Snowpipe costs.
Maximizing micro-partitions is not configurable directly and is counterproductive for cost and performance. For SnowPro Architect candidates, this question emphasizes the importance of batching and file sizing strategies when designing streaming ingestion pipelines with Kafka and Snowpipe.
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QUESTION NO: 18 [Security and Access Management]
An Architect is creating a new database role and is considering using the OR REPLACE keywords in the CREATE DATABASE ROLE command.
What should be considered before using OR REPLACE?
A.
The dropped database role cannot be recreated.
B.
Recreating a database role drops it from any shares it is granted to.
C.
The OR REPLACE keywords are unsupported for database roles.
D.
The database role can only be dropped by a role with MANAGE GRANTS privilege.
Answer: B
Using CREATE OR REPLACE DATABASE ROLE in Snowflake drops and recreates the role if it already exists. While this can simplify deployment scripts, it has important side effects that architects must understand. When a database role is dropped and recreated, all grants associated with that role—including grants to shares—are removed (Answer B). This can unintentionally break data sharing configurations or downstream access.
Although the role itself can be recreated, the loss of grants requires manual remediation, increasing operational risk. The OR REPLACE syntax is supported for database roles, and while privilege requirements matter, they are not the key risk being tested here.
For SnowPro Architect candidates, this question reinforces best practices around role lifecycle management and governance. In production environments, replacing roles should be done cautiously, with a clear understanding of grant dependencies and the potential impact on security and data sharing.
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QUESTION NO: 19 [Performance Optimization and Monitoring]
A large table is accessed by multiple teams using point-lookup queries on different columns. Query performance is poor.
What can be done to ensure good performance for all teams?
A.
Build multiple materialized views with cluster keys on each column.
B.
Build materialized views and allow Snowflake to replace the base table.
C.
Use the search optimization service on the underlying table.
D.
Create clustering keys using a combination of all lookup columns.
Answer: C
When multiple teams perform selective point-lookups on different columns of the same large table, traditional clustering is not effective because a table can have only one clustering key. Creating a composite clustering key across many columns often leads to poor clustering depth and limited pruning benefits.
The Search Optimization Service (SOS) is specifically designed for this scenario (Answer C). It enables fast point-lookups across multiple columns without requiring table reclustering. SOS maintains additional search access paths that accelerate highly selective predicates and is ideal when queries vary across many lookup columns.
Materialized views introduce additional storage, maintenance overhead, and operational complexity, and Snowflake does not automatically replace base tables with materialized views. This question tests an architect’s ability to choose the right optimization technique for multi-access-path workloads, a core SnowPro Architect competency.
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QUESTION NO: 20 [Snowflake Ecosystem and Integrations]
What is a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka?
A.
The Connector only works in AWS regions.
B.
The Connector works with all file formats.
C.
The Connector creates and manages its own stage, file format, and pipe objects.
D.
Loads using the Connector have lower latency than Snowpipe and ingest data in real time.
Answer: C
The Snowflake Connector for Kafka simplifies streaming ingestion by automatically managing Snowflake objects required for ingestion. It creates and manages internal stages, file formats, and Snowpipe pipes without requiring manual configuration (Answer C). This reduces operational overhead and simplifies deployment.
The connector is cloud-agnostic and works across supported Snowflake clouds, not just AWS. It supports a defined set of formats (such as JSON and Avro), not all possible formats. While the connector enables near real-time ingestion, it still uses Snowpipe under the hood and does not guarantee lower latency than Snowpipe Streaming.
For SnowPro Architect candidates, this question emphasizes understanding how Snowflake-managed connectors abstract infrastructure details while still relying on Snowflake-native ingestion services.
