ARA-C01 Quick Quiz — 10 Questions

Question 1

Refer to the exhibit. A query profile shows that 'partitions_scanned' is 5,000 while 'partitions_total' is 5,000 for a specific TableScan operator. What architectural issue does this indicate, and what is the recommended solution?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Data is poorly clustered for the query filters; define a Cluster Key.. When the number of scanned partitions equals the total partitions, it means that no pruning occurred, and Snowflake performed a full table scan. Architecturally, this usually happens because the query filter does not align with the way data is organized in micro-partitions. Defining a Cluster Key on the columns used in the filter is the standard architectural fix to enable efficient pruning.

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The warehouse is too small; increase it to improve the scan speed.

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The query is hitting the metadata cache; no action is required.

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The table is a Transient table; convert it to a Permanent table.

Question 2

A data engineer is observing high costs associated with Snowpipe for a high-volume ingestion pipeline where many small files arrive every second. What is the most effective architectural change to reduce Snowpipe costs while maintaining near real-time ingestion?

Accepted Answer

Implement a file grouping strategy in the cloud storage layer. Snowpipe costs are influenced by the number of files processed due to per-file overhead. By aggregating small files into larger batches at the source or using a more efficient staging strategy, architects can reduce the overhead. Monitoring the pipe's utilization and file sizing is a core responsibility for optimizing serverless compute usage in Snowflake.

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Increase the size of the virtual warehouse used by Snowpipe

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Switch from Snowpipe to a scheduled COPY INTO command

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Use the PURGE = TRUE option in the Snowpipe definition

Question 3

A database architect needs to design a high-churn table that receives millions of updates daily. To minimize storage costs associated with Fail-safe and Time Travel while maintaining some recovery capability, which table type and configuration should be used?

Accepted Answer

Transient table with Time Travel set to 1 day.. Transient tables are designed for data that is important but does not require the high level of protection provided by Fail-safe. They support Time Travel (up to 1 day) but do not have a Fail-safe period. For high-churn tables, this significantly reduces storage costs because Fail-safe records all changes for 7 days, which can be expensive with frequent updates.

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Permanent table with Time Travel set to 0 days.

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Temporary table with Time Travel set to 90 days.

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Permanent table with a cluster key defined on the update timestamp.

Question 4

Which THREE factors should be considered when evaluating the cost-benefit of enabling the Search Optimization Service on a large table? (Choose three.)

Accepted Answer

The frequency and selectivity of point lookup queries.. The Search Optimization Service is a powerful tool, but it carries costs related to storage and maintenance. Enabling it for columns with very low selectivity (e.g., boolean flags) provides little benefit while incurring continuous costs. Architects must balance the gain in query performance for point lookups against the DML overhead and the additional storage required to maintain the index structures.

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The total number of rows in the table.

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The number of concurrent users accessing the warehouse.

Question 5

What is the primary benefit of using Materialized Views in Snowflake for performance optimization?

Accepted Answer

They reduce compute costs for frequently run, complex queries.. Materialized views precompute results for complex or expensive queries. By storing the results in a persistent format that is automatically maintained by Snowflake, subsequent queries against the view can avoid the heavy computational cost of the underlying query. This is particularly beneficial for queries that involve heavy aggregations or complex filtering that are executed frequently by users.

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They provide real-time updates for all data types.

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They automatically increase warehouse size during load.

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They bypass the need for any indexing on base tables.

Question 6

A large-scale data ingestion pipeline is experiencing intermittent queuing. Which architectural element should be analyzed to identify the source of the contention?

Accepted Answer

The virtual warehouse load statistics.. Queuing in Snowflake occurs when the virtual warehouse lacks sufficient capacity to handle the number of concurrent queries submitted. Analyzing the Query History and the Warehouse Load statistics allows the architect to see if the warehouse is saturated. If the warehouse is maxed out on concurrency, the architect can either scale up (increase size) or scale out (multi-cluster warehouse) to provide additional compute resources to accommodate the ingestion load effectively.

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The Cloud Services metadata repository.

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The object storage latency from the cloud provider.

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The internal state of the micro-partition pruning engine.

Question 7

An organization wants to centralize user management by integrating Snowflake with their corporate Okta instance using SCIM. Which architectural component facilitates the synchronization of user metadata between Okta and Snowflake?

Accepted Answer

Snowflake SCIM provisioning integration.. SCIM (System for Cross-domain Identity Management) is an open standard that allows for the automation of user provisioning. By using the Snowflake SCIM integration, the architect ensures that user additions, updates, and deletions in Okta are automatically reflected in Snowflake. This eliminates manual administrative overhead and reduces the risk of orphaned accounts or stale permissions, which are common security vulnerabilities in large organizations where employee turnover is high and manual tracking is prone to errors.

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Snowflake Data Share.

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External OAuth security integration.

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Snowflake Native App.

Question 8

Which TWO of the following statements about the Query Acceleration Service are correct? (Choose two.)

Accepted Answer

It is intended for queries with large, compute-intensive scan or aggregation operations.. The Query Acceleration Service is an opt-in feature designed to improve the performance of complex, large-scale scan and aggregation queries. It works by dynamically providing extra compute resources to a query that would otherwise be throttled. It is only useful for queries with high variance in performance, where specific parts of the query take significantly longer than the rest of the execution.

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It automatically scales up the warehouse size for all queries.

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It effectively replaces the need for clustering keys.

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It is always free of charge.

Question 9

Which TWO of the following are true regarding the use of Snowflake Data Shares for security purposes?

Accepted Answer

Data shares eliminate the need for data duplication.. Data Shares provide a secure way to share data without copying it. The provider account retains full control over the data objects, and the consumer account gets read-only access. This architecture is inherently more secure than traditional ETL-based data transfer methods because it eliminates the movement of data, reduces the risk of data leakage during transit, and ensures that the consumer is always querying the most up-to-date version of the data provided by the source.

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Data shares allow consumers to write back to the source.

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Data shares automatically copy data to the consumer.

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Shares require public internet access.

Question 10

An architect needs to optimize a dashboard that frequently queries a multi-terabyte table. The queries involve complex aggregations on several columns and a join to a small dimension table. The dashboard allows users to filter by any combination of five different dimensions. Which optimization strategy is most appropriate?

Accepted Answer

Implement a Materialized View that pre-aggregates the data by the five dimensions.. Materialized Views are highly effective for queries that involve complex aggregations and joins on large datasets where the results can be pre-calculated. Unlike the Search Optimization Service, which is for point lookups, Materialized Views store the actual result of the query. This significantly reduces the compute required at runtime for dashboards with repetitive aggregation patterns.

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Enable Search Optimization on all five dimension columns.

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Use a Cluster Key on the dimension table to speed up the joins.

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Increase the Warehouse size to reduce the time for aggregations.