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Snowflake ARA-C01 Exam Questions
- Topic 1: Design data sharing solutions, based on different use cases/ Determine the appropriate data transformation solution to meet business needs
- Topic 2: Design a Snowflake account and database strategy, based on business requirements/ Troubleshoot performance issues with existing architectures
- Topic 3: Outline the benefits and limitations of various data models in a Snowflake environment/ Outline key tools in Snowflake’s ecosystem and how they interact with Snowflake
- Topic 4: Outline performance tools, best practices, and appropriate scenarios where they should be applied/ Determine the appropriate data recovery solution in Snowflake and how data can be restored
- Topic 5: Determine the appropriate data loading or data unloading solution to meet business needs/ Design an architecture that meets data security, privacy, compliance, and governance requirements
- Topic 6: Create architecture solutions that support Development Lifecycles as well as workload requirements/ Outline Snowflake security principles and identify use cases where they should be applied
Free Snowflake ARA-C01 Exam Actual Questions
Note: Premium Questions for ARA-C01 were last updated On Jul. 01, 2025 (see below)
The Business Intelligence team reports that when some team members run queries for their dashboards in parallel with others, the query response time is getting significantly slower What can a Snowflake Architect do to identify what is occurring and troubleshoot this issue?
A)
B)
C)
D)
A company is using Snowflake in Azure in the Netherlands. The company analyst team also has data in JSON format that is stored in an Amazon S3 bucket in the AWS Singapore region that the team wants to analyze.
The Architect has been given the following requirements:
1. Provide access to frequently changing data
2. Keep egress costs to a minimum
3. Maintain low latency
How can these requirements be met with the LEAST amount of operational overhead?
What does a Snowflake Architect need to consider when implementing a Snowflake Connector for Kafka?
A company is trying to Ingest 10 TB of CSV data into a Snowflake table using Snowpipe as part of Its migration from a legacy database platform. The records need to be ingested in the MOST performant and cost-effective way.
How can these requirements be met?
For ingesting a large volume of CSV data into Snowflake using Snowpipe, especially for a substantial amount like 10 TB, the on error = SKIP_FILE option in the COPY INTO command can be highly effective. This approach allows Snowpipe to skip over files that cause errors during the ingestion process, thereby not halting or significantly slowing down the overall data load. It helps in maintaining performance and cost-effectiveness by avoiding the reprocessing of problematic files and continuing with the ingestion of other data.
At which object type level can the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY and APPLY SESSION POLICY privileges be granted?
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