ARA-C01 Practice Test 2 — 10 Questions

Question 1

A Business Critical edition customer wants to implement a disaster recovery strategy that ensures their Snowflake account can failover to a different region with a Recovery Time Objective (RTO) of less than 1 hour. Which architectural feature is required?

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

Accepted Answer

Failover Groups, which replicate databases and account-level objects.. Snowflake's Failover Groups (a part of Business Continuity) allow architects to replicate not just data (databases), but also account-level metadata like users, roles, and warehouse configurations to a standby account in a different region. In the event of a regional outage, the architect can promote the standby account to primary, providing a fast and comprehensive failover capability.

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Database Replication, which synchronizes tables across regions.

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Client Redirect, which provides a single connection URL for failover.

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Tri-Secret Secure, which ensures data is encrypted during regional transfer.

Question 2

A query is performing poorly due to 'Remote Disk I/O'. The architect notices that the query profile shows a large number of micro-partitions being scanned. The table is already clustered on the filter column. What is the most likely cause of the high Remote Disk I/O?

Accepted Answer

The warehouse cache is cold or the data exceeds the local cache capacity.. Remote Disk I/O occurs when a Virtual Warehouse must pull data from the Storage layer (S3/Azure/GCP) because it is not available in the local SSD cache. Even if a table is clustered, if the local cache is 'cold' or if the warehouse is too small to hold the working set, the system will constantly fetch data from the remote storage.

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The metadata layer is overloaded and cannot provide pruning information.

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The table has too many small files, causing metadata bloat.

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The clustering depth is 1.0, which indicates the table is perfectly clustered.

Question 3

An architect is designing a staging area for a daily ETL process where data is loaded, transformed, and then moved to a permanent production table. The staging data is only needed for 24 hours and does not require long-term Fail-safe protection. Which table type is most cost-effective?

Accepted Answer

Transient Tables. Transient tables are ideal for staging environments because they persist across sessions but do not incur the costs associated with Fail-safe storage. This makes them significantly cheaper for high-churn data that can be easily recreated if a system failure occurs, while still allowing for multi-day processing if needed.

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Permanent Tables

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Temporary Tables

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External Tables

Question 4

When a query is executed, which layer is responsible for the 'Query Plan' generation?

Accepted Answer

The Cloud Services layer.. The Cloud Services layer is responsible for query optimization and the generation of the query execution plan. It compiles the SQL into a set of steps that the virtual warehouse will execute. This planning stage involves analyzing the metadata, understanding data distribution, and selecting the most efficient path for data retrieval, which is a critical function that ensures high performance across diverse and complex data workloads in Snowflake.

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The Database Storage layer.

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The Virtual Warehouse layer.

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The Result Cache.

Question 5

A Snowflake architect notices that a recurring ETL job that loads data into a table and then immediately runs a complex aggregation query is taking longer than expected. The table is not clustered, and the query filters on a timestamp column. Which action would most directly improve the performance of the aggregation query?

Accepted Answer

Adding a clustering key on the timestamp column. Clustering the table on the timestamp column enables partition pruning, so the query only scans micro-partitions that contain the relevant time range. This directly reduces I/O and improves aggregation performance. Search Optimization is for point lookups, larger warehouses add compute but not pruning, and multi-cluster addresses concurrency.

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Increasing the warehouse size

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Using a multi-cluster warehouse

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Enabling the Search Optimization Service on the table

Question 6

A financial services company uses Snowflake to analyze trade data. They have a large table TRANSACTIONS with a clustering key on TRADE_DATE. Queries that filter on TRADE_DATE and ACCOUNT_ID are performing well, but queries that filter only on ACCOUNT_ID are slow. The architect wants to improve performance for ACCOUNT_ID-only queries without degrading the performance of TRADE_DATE queries. Which solution is most appropriate?

Accepted Answer

Create a search optimization service on the ACCOUNT_ID column.. Search Optimization Service is the ideal solution for accelerating selective queries on columns that are not part of the clustering key. It creates a persistent search access path that allows Snowflake to quickly locate micro-partitions containing the desired values. This improves ACCOUNT_ID-only queries without altering the existing clustering on TRADE_DATE, thus preserving performance for TRADE_DATE queries.

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Add a secondary clustering key on ACCOUNT_ID.

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Add a materialized view that filters on ACCOUNT_ID and pre-aggregates the data.

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Change the clustering key to (ACCOUNT_ID, TRADE_DATE).

Question 7

A SnowPro Advanced Architect is tuning a virtual warehouse that experiences high concurrency during peak hours. The architect observes that queries are queuing, and the warehouse is not fully utilizing its resources. Which TWO actions should the architect take to improve concurrency? (Choose two.)

Accepted Answer

Enable multi-cluster warehouse mode and set a minimum and maximum cluster count.. High concurrency with queuing is best addressed by scaling out compute resources. Multi-cluster warehouses automatically add clusters to handle queuing, and setting min/max clusters controls the scale. Separating workloads onto different warehouses distributes load and reduces contention. Scaling up, auto-suspend, and Query Acceleration Service do not directly improve concurrency; they address different problems such as per-query performance, cost control, or specific query offloading.

Answer

Enable the Query Acceleration Service on the warehouse to handle queued queries.

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Increase the warehouse size to add more compute nodes per cluster.

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Set the warehouse to auto-suspend after a short period to free up resources for other warehouses.

Question 8

A data architect is designing a table that will store 10TB of semi-structured JSON data in a VARIANT column. Queries frequently filter on specific JSON attributes using dot notation, such as data:customer_id::string. The architect wants to minimize query latency and storage costs. Which approach should the architect take?

Accepted Answer

Extract frequently queried JSON attributes into separate relational columns and cluster on those columns.. Extracting frequently queried JSON attributes into native columns and clustering on them allows Snowflake to prune micro-partitions efficiently, reducing the amount of data scanned. This improves query latency and can reduce storage costs because native columns are more compact than VARIANT. Other options either add overhead or do not address the need for efficient filtering on specific attributes.

Answer

Use a materialized view that pre-aggregates the JSON data by customer_id.

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Enable the Search Optimization Service on the VARIANT column to accelerate point lookups.

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Store the JSON data in an external table and query it directly with Snowflake.

Question 9

A data engineer is experiencing slow query performance on a large table. The query filters by a high-cardinality column that is not the clustering key. Which optimization technique should the engineer prioritize to improve performance?

Accepted Answer

Enable the Search Optimization Service on the table.. Search optimization service is the ideal choice for high-cardinality columns used in point-lookup queries. Unlike clustering, which reorders data, the search optimization service creates a persistent search index, allowing Snowflake to prune partitions effectively even when queries do not align with the table's natural clustering key. This reduces total scanning and improves response times for point-lookups on large tables significantly.

Answer

Increase the warehouse size to maximum.

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Apply a materialized view with a filter.

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Change the table clustering key to the high-cardinality column.

Question 10

A Snowflake architect is optimizing a slow query that performs a large aggregation over a fact table. The query profile shows that the Aggregation operator is taking a long time. Which TWO actions are most likely to improve the performance of this aggregation? (Choose two.)

Accepted Answer

Use a materialized view that pre-aggregates the data.. Clustering on the GROUP BY columns improves data locality, reducing shuffle during aggregation, while a materialized view that pre-aggregates can avoid computing the aggregation altogether for matching queries. Both directly target the aggregation bottleneck. Scaling up or enabling Search Optimization Service do not address the core issue of data organization for aggregation.

Answer

Rewrite the query to use a window function instead of GROUP BY.

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Increase the size of the virtual warehouse to add more compute resources.

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Enable the Search Optimization Service on the fact table.