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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 Apr. 18, 2025 (see below)
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?
Two queries are run on the customer_address table:
create or replace TABLE CUSTOMER_ADDRESS ( CA_ADDRESS_SK NUMBER(38,0), CA_ADDRESS_ID VARCHAR(16), CA_STREET_NUMBER VARCHAR(IO) CA_STREET_NAME VARCHAR(60), CA_STREET_TYPE VARCHAR(15), CA_SUITE_NUMBER VARCHAR(10), CA_CITY VARCHAR(60), CA_COUNTY
VARCHAR(30), CA_STATE VARCHAR(2), CA_ZIP VARCHAR(10), CA_COUNTRY VARCHAR(20), CA_GMT_OFFSET NUMBER(5,2), CA_LOCATION_TYPE
VARCHAR(20) );
ALTER TABLE DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS ADD SEARCH OPTIMIZATION ON SUBSTRING(CA_ADDRESS_ID);
Which queries will benefit from the use of the search optimization service? (Select TWO).
The use of the search optimization service in Snowflake is particularly effective when queries involve operations that match exact substrings or start from the beginning of a string. The ALTER TABLE command adding search optimization specifically for substrings on the CA_ADDRESS_ID field allows the service to create an optimized search path for queries using substring matches.
Option A benefits because it directly matches a substring from the start of the CA_ADDRESS_ID, aligning with the optimization's capability to quickly locate records based on the beginning segments of strings.
Option B also benefits, despite performing a full equality check, because it essentially compares the full length of CA_ADDRESS_ID to a substring, which can leverage the substring index for efficient retrieval. Options C, D, and E involve patterns that do not start from the beginning of the string or use negations, which are not optimized by the search optimization service configured for starting substring matches. Reference: Snowflake's documentation on the use of search optimization for substring matching in SQL queries.
A user has the appropriate privilege to see unmasked data in a column.
If the user loads this column data into another column that does not have a masking policy, what will occur?
According to the SnowPro Advanced: Architect documents and learning resources, column masking policies are applied at query time based on the privileges of the user who runs the query. Therefore, if a user has the privilege to see unmasked data in a column, they will see the original data when they query that column. If they load this column data into another column that does not have a masking policy, the unmasked data will be loaded in the new column, and any user who can query the new column will see the unmasked data as well. The masking policy does not affect the underlying data in the column, only the query results.
Snowflake Documentation: Column Masking
Snowflake Learning: Column Masking
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