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  1. Home
  2. Snowflake Certification
  3. ARA-C01 Exam
  4. Snowflake.ARA-C01.v2026-04-11.q236 Dumps
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Question 186

What Snowflake system functions are used to view and or monitor the clustering metadata for a table? (Select TWO).

Correct Answer: C,E
The Snowflake system functions used to view and monitor the clustering metadata for a table are:
SYSTEM$CLUSTERING_INFORMATION
SYSTEM$CLUSTERING_DEPTH
Comprehensive But Short Explanation:
The SYSTEM$CLUSTERING_INFORMATION function in Snowflake returns a variety of clustering information for a specified table. This information includes the average clustering depth, total number of micro-partitions, total constant partition count, average overlaps, average depth, and a partition depth histogram. This function allows you to specify either one or multiple columns for which the clustering information is returned, and it returns this data in JSON format.
The SYSTEM$CLUSTERING_DEPTH function computes the average depth of a table based on specified columns or the clustering key defined for the table. A lower average depth indicates that the table is better clustered with respect to the specified columns. This function also allows specifying columns to calculate the depth, and the values need to be enclosed in single quotes.
Reference:
SYSTEM$CLUSTERING_INFORMATION: Snowflake Documentation
SYSTEM$CLUSTERING_DEPTH: Snowflake Documentation
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Question 187

A table contains five columns and it has millions of records. The cardinality distribution of the columns is shown below:

Column C4 and C5 are mostly used by SELECT queries in the GROUP BY and ORDER BY clauses.
Whereas columns C1, C2 and C3 are heavily used in filter and join conditions of SELECT queries.
The Architect must design a clustering key for this table to improve the query performance.
Based on Snowflake recommendations, how should the clustering key columns be ordered while defining the multi-column clustering key?

Correct Answer: C
According to the Snowflake documentation, the following are some considerations for choosing clustering for a table1:
* Clustering is optimal when either:
* You require the fastest possible response times, regardless of cost.
* Your improved query performance offsets the credits required to cluster and maintain the table.
* Clustering is most effective when the clustering key is used in the following types of query predicates:
* Filter predicates (e.g. WHERE clauses)
* Join predicates (e.g. ON clauses)
* Grouping predicates (e.g. GROUP BY clauses)
* Sorting predicates (e.g. ORDER BY clauses)
* Clustering is less effective when the clustering key is not used in any of the above query predicates, or when the clustering key is used in a predicate that requires a function or expression to be applied to the key (e.g. DATE_TRUNC, TO_CHAR, etc.).
* For most tables, Snowflake recommends a maximum of 3 or 4 columns (or expressions) per key.
Adding more than 3-4 columns tends to increase costs more than benefits.
Based on these considerations, the best option for the clustering key columns is C. C1, C3, C2, because:
* These columns are heavily used in filter and join conditions of SELECT queries, which are the most effective types of predicates for clustering.
* These columns have high cardinality, which means they have many distinct values and can help reduce the clustering skew and improve the compression ratio.
* These columns are likely to be correlated with each other, which means they can help co-locate similar rows in the same micro-partitions and improve the scan efficiency.
* These columns do not require any functions or expressions to be applied to them, which means they can be directly used in the predicates without affecting the clustering.
References: 1: Considerations for Choosing Clustering for a Table | Snowflake Documentation
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Question 188

An Architect with the ORGADMIN role wants to change a Snowflake account from an Enterprise edition to a Business Critical edition.
How should this be accomplished?

Correct Answer: D
To change the edition of a Snowflake account, an organization administrator (ORGADMIN) cannot directly alter the account settings through SQL commands or the Snowflake interface. The proper procedure is to contact Snowflake Support to request an edition change for the account. This ensures that the change is managed correctly and aligns with Snowflake's operational protocols.
References: This process is outlined in the Snowflake documentation, which specifies that changes to an account's edition should be facilitated through Snowflake Support1.
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Question 189

An Architect needs to meet a company requirement to ingest files from the company's AWS storage accounts into the company's Snowflake Google Cloud Platform (GCP) account. How can the ingestion of these files into the company's Snowflake account be initiated? (Select TWO).

Correct Answer: A,C
This question centers aroundcross-cloud ingestionusingSnowpipe, where the Snowflake account is onGoogle Cloud, but the source data resides inAmazon S3. Sinceautomatic Snowpipe event-based integration (using AWS SNS)is only supported when Snowflake is deployed onAWS, this limits the options.
In a multi-cloud scenario (e.g., S3 to GCP-hosted Snowflake), onlymanual triggering of Snowpipeis available, typically through theSnowpipe REST API.
Option A - Correct
You can configure your client application to call theSnowpipe REST APIwhen files are available. This is suitable and supported forcross-cloud ingestion, such as from AWS S3 into a Snowflake account hosted on GCP.
Official Extract:
"For cloud platforms other than AWS, or when using Snowpipe in a cross-cloud or external storage configuration, the REST API must be used to trigger ingestion." Source:Snowflake Docs - Using Snowpipe REST API Option C - Correct AWS Lambda can be used toinvoke the Snowpipe REST APIwhen files arrive in S3. This is a common pattern in cross-cloud integrations, acting as a middle layer.
Official Extract:
"You can use an AWS Lambda function to call the Snowpipe REST API when new files are added to your S3 bucket. This pattern works for Snowflake accounts hosted outside of AWS." Source:Snowflake Docs - Automating Snowpipe REST API Calls Option B - Incorrect S3 Glacier is an archival storage service and isnot supportedfor direct loading into Snowflake, since data is not immediately accessible.
Option D - Incorrect
UsingAWS SNS for auto-notificationis only supported whenSnowflake is deployed on AWS. Since this question specifies Snowflake onGCP, SNS-based integration isnot supported.
Official Extract:
"Snowpipe supports Amazon S3 event notifications (SNS/SQS) only for Snowflake accounts hosted on AWS." Source:Snowflake Docs - Supported Cloud Platforms Option E - Incorrect Similar to Option B, S3 Glacier isnot a valid sourcefor data loading because it does not provide real-time file access.
References:
Snowflake Documentation - Snowpipe Overview
Snowpipe REST API
Automating Snowpipe with Lambda
Automatic Ingestion Support - Cloud Limitations
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Question 190

An Architect is defining transaction rules to adhere to ACID properties to ensure that executed statements are either committed or rolled back. Based on this scenario, what characteristics of transactions should be considered? (Select TWO).

Correct Answer: C,D
Comprehensive and Detailed 150 to 250 words of Explanation From Snowflake SnowPro Architect exam scope and all publicly documented material:
Snowflake supports transactional behavior for DML (and SELECT statements within a transaction context), but DDL statements have special behavior: many DDL operations implicitly commit the current transaction.
Because of that, mixing DDL with DML inside an explicit transaction undermines the "all-or-nothing" rollback expectations and complicates ACID-driven rules. Therefore, a key design characteristic is that explicit transactions should generally be limited to DML and query statements, while recognizing that DDL can implicitly commit and break transactional boundaries (Choice C). Additionally, Snowflake supports explicit transaction control statements using standard SQL forms; BEGIN WORK and COMMIT WORK are valid ways to open and close an explicit transaction (Choice D). From an architecting standpoint, this matters for pipeline design, error handling, and idempotency: multi-step loads/merges should group logically atomic DML steps into an explicit transaction so they commit together or roll back together, but DDL (like CREATE
/ALTER) should be separated or executed with full awareness of implicit commits. This is consistent with SnowPro Architect expectations around reliable data engineering patterns and correctness under failure.
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