ARA-C01 Practice Test 13 — 15 Questions

Question 1

A Snowflake architect is configuring a secure data sharing setup between a provider account and a consumer account in a different region. The provider wants to share a database that contains sensitive financial data. The architect must ensure that the data is encrypted in transit and at rest, and that the consumer cannot download the shared data. Which Snowflake feature should the architect use to meet these requirements?

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

Accepted Answer

Secure Data Sharing. Secure Data Sharing is the correct feature because it enables sharing live, read-only data between Snowflake accounts without copying it. The data remains in the provider's account, encrypted at rest and in transit, and consumers cannot download the shared data. This ensures both security and compliance. Other options either involve data duplication or do not provide the same level of control over data access.

Answer

Database Replication

Answer

Snowflake Data Marketplace

Answer

External Tables with a Secure View

Question 2

An architect is building a Dynamic Table that reads from a base table receiving continuous inserts. The refresh is configured with TARGET_LAG = '1 minute' and the warehouse is a dedicated XSMALL. Monitoring shows refreshes frequently take longer than one minute and sometimes overlap with the next scheduled run. The team wants to reduce refresh latency without changing the query logic. Which change is most appropriate?

Accepted Answer

Increase the warehouse size used by the Dynamic Table so that each refresh completes within the target lag window.. Dynamic Table refreshes execute on the warehouse tied to the object, so when a run cannot finish within TARGET_LAG the practical remedy is more compute. Scaling the warehouse up shortens each incremental refresh and keeps runs from overlapping. Changing refresh mode to full, altering initialization behavior, or replacing the object with a task-driven view does not reduce per-run duration and in some cases increases cost or complexity.

Answer

Set INITIALIZE to ON_CREATE and recreate the Dynamic Table so that the initial population happens at creation time.

Answer

Set REFRESH_MODE to FULL on the Dynamic Table so that each run recomputes the entire result set instead of applying incremental changes.

Answer

Convert the Dynamic Table to a regular view and schedule a task to run every minute to materialize the results into a physical table.

Question 3

A financial institution uses Snowflake with Tri-Secret Secure backed by a customer-managed key in AWS KMS. The security team wants to rotate the customer-managed key without causing downtime or requiring re-encryption of all data. What is the correct procedure?

Accepted Answer

Rotate the customer-managed key by creating a new key version in AWS KMS; Snowflake will automatically use the new version for new data and continue to decrypt old data using the old version.. Tri-Secret Secure uses a customer-managed key in AWS KMS to add an extra layer of encryption. Key rotation in KMS is achieved by creating a new key version. Snowflake can use the new version for new data while still decrypting old data with previous versions, because KMS retains all versions. This allows seamless rotation without downtime or data re-encryption. The other options involve incorrect commands or unnecessary re-encryption.

Answer

Create a new customer-managed key in AWS KMS, update the Snowflake account to use the new key, and Snowflake will automatically re-encrypt all data in the background.

Answer

Disable Tri-Secret Secure, rotate the key, and then re-enable Tri-Secret Secure; this will automatically re-wrap all data encryption keys with the new key.

Answer

Manually re-encrypt all data by running ALTER ACCOUNT SET ENCRYPTION_KEY, which triggers a full re-encryption process.

Question 4

What happens when a Virtual Warehouse is 'resized' to a larger size while queries are currently running on it?

Accepted Answer

Running queries continue on the original resources; new queries use the new size.. Resizing a warehouse is a dynamic operation. When you increase the size, Snowflake adds new compute nodes to the existing cluster. The currently running queries continue to run on the old nodes, while all new queries submitted after the resize operation begin using the new, larger capacity. This allows for seamless scaling without interrupting or failing active queries, which is critical for high-availability production environments requiring zero downtime.

Answer

All running queries are terminated and restarted.

Answer

The warehouse is paused until all current queries finish.

Answer

The warehouse creates a new temporary warehouse.

Question 5

What is the primary architectural advantage of using External Tables with the 'REFRESH' property instead of traditional data loading via COPY INTO?

Accepted Answer

It allows for querying data in place without incurring Snowflake storage costs.. External tables allow Snowflake to query data directly from cloud storage without importing it into Snowflake's proprietary storage format. This is beneficial for data lake architectures where data must remain accessible to other tools. The REFRESH property ensures that the Snowflake metadata stays in sync with the files in the cloud bucket as they are added or removed.

Answer

External tables provide significantly faster query performance than internal tables.

Answer

External tables automatically support ACID transactions for updates and deletes.

Answer

Using external tables eliminates the need for a Virtual Warehouse for queries.

Question 6

Which architectural component is responsible for managing the ACID properties across distributed compute nodes during a complex DML operation?

Accepted Answer

The Cloud Services layer.. The Cloud Services layer acts as the orchestrator for all transactions in Snowflake. It coordinates across the distributed compute resources to ensure atomicity, consistency, isolation, and durability. By managing the global transaction state and the metadata of micro-partitions, the Cloud Services layer ensures that even when multiple warehouses or users are modifying the same data, the state remains consistent and conflict-free, which is essential for multi-user analytical workloads.

Answer

The virtual warehouse compute engine.

Answer

The cloud storage object repository.

Answer

The micro-partition header files.

Question 7

Which architectural feature ensures that DML operations in Snowflake do not impact query performance and provide data consistency without the need for manual locking?

Accepted Answer

The use of micro-partition immutability and MVCC.. Snowflake's multi-version concurrency control (MVCC) architecture, combined with the immutability of micro-partitions, allows read and write operations to coexist seamlessly. When a DML operation modifies data, Snowflake does not update existing partitions but instead creates new ones. This design avoids the need for table-level locks, ensuring that read-only queries always see a consistent snapshot of the data based on the commit time of the transaction.

Answer

The use of explicit table-level locks controlled by the Cloud Services layer.

Answer

The integration of a distributed cache that manages row-level locks.

Answer

The implementation of a primary index for every table that serializes writes.

Question 8

Refer to the exhibit. Based on the configuration provided, how will the multi-cluster warehouse behave when the query load increases?

Accepted Answer

A second cluster will only start if the system estimates it will remain busy for 6 minutes.. The Economy scaling policy is designed to conserve credits by only starting a new cluster if Snowflake estimates there is enough concurrent load to keep the new cluster busy for at least six minutes. This contrasts with the Standard policy, which starts clusters immediately to minimize queuing, making Economy better for workloads where latency is less critical.

Answer

A new cluster will start immediately as soon as a single query is queued.

Answer

The warehouse will scale up to an X-Large size to handle the increased load.

Answer

All five clusters will start simultaneously when the first query is submitted to the warehouse.

Question 9

Refer to the exhibit. An architect attempts to connect to Snowflake from 10.0.0.5. Based on the configuration, what will happen?

Accepted Answer

The connection is rejected.. Snowflake evaluates network policies by checking the blocked list first, then the allowed list. Because the IP 10.0.0.5 falls within the 10.0.0.0/8 range defined in the blocked_ip_list, the connection request is rejected immediately. Even if the IP were also in an allowed range, the explicit block takes precedence, ensuring that known malicious or restricted subnets are denied access regardless of other configuration settings within the specific network policy applied to the account.

Answer

The connection is successful.

Answer

The connection depends on the user's role.

Answer

The connection is deferred to the secondary policy.

Question 10

Which THREE of the following functions are primarily handled by the Snowflake Cloud Services layer? (Choose THREE)

Accepted Answer

Authentication and access control for users and applications.. The Cloud Services layer is the 'brain' of Snowflake, managing the overall state and coordination of the system. It handles tasks that do not require the heavy computational power of virtual warehouses, such as security, metadata management, and query optimization. Understanding the separation between compute, storage, and services is fundamental to passing the Architect exam and optimizing Snowflake costs.

Answer

Executing complex JOIN operations between large tables.

Answer

Physically storing the data in encrypted S3 or Azure Blobs.

Question 11

A global enterprise is consolidating multiple Snowflake accounts into a single Organization to optimize billing and data sharing. They need to move a large production database from an account in 'aws_us_east_1' to an account in 'azure_west_us'. What is the most efficient architectural approach to achieve this while maintaining data consistency?

Accepted Answer

Enable database replication between the source AWS account and the target Azure account within the Organization.. Consolidating multiple Snowflake accounts into a single Organization requires a strategy for moving data across regions. Snowflake's cross-region database replication is the primary mechanism for this, as it handles the underlying cloud provider differences and ensures data integrity through asynchronous synchronization. This architectural approach minimizes downtime and prevents the complexities of manual export/import processes while maintaining a single source of truth.

Answer

Unload data to a cross-region S3 bucket and use the COPY INTO command to load it into the Azure account.

Answer

Use Snowflake Data Sharing to share the database from the AWS account to the Azure account directly.

Answer

Create a Clone of the database and use the Snowflake CLI to push the metadata to the Azure account.

Question 12

A data engineer is designing an automated ingestion pipeline using Snowpipe Streaming to ingest high-frequency clickstream data from Kafka into Snowflake tables. The architecture requires low latency and cost-effective continuous loading. Which underlying Snowflake architectural feature makes Snowpipe Streaming uniquely capable of bypassing the traditional internal staging phase?

Accepted Answer

It writes data directly to internal table micro-partitions via client-side API calls, avoiding file staging.. Snowpipe Streaming writes directly to Snowflake micro-partitions using native Java APIs without requiring files to be staged first in internal stages. This architectural shortcut significantly reduces latency and compute costs for real-time streaming pipelines compared to standard file-based Snowpipe loading.

Answer

It leverages serverless tasks to automatically stage and bulk load streaming batches every minute.

Answer

It requires continuous execution of a dedicated virtual warehouse to transform and commit micro-batches.

Answer

It automatically converts incoming streaming payloads into Parquet files before final table insertion.

Question 13

Which of the following describes the correct behavior of a Masking Policy applied to a column that is also referenced in a Row Access Policy?

Accepted Answer

The Row Access Policy is evaluated first.. Snowflake enforces policies in a specific sequence. When both Row Access and Masking Policies are present, the Row Access Policy is evaluated first to determine the visible rows, and then the Masking Policy is applied to the visible data. This ensures that the security constraints are applied logically and cumulatively. This behavior is crucial for preventing information leakage, ensuring that users cannot bypass row-level filters by using column-level masking logic or vice versa, providing a consistent security model.

Answer

The Masking Policy is evaluated first.

Answer

Only the Masking Policy is applied.

Answer

Only the Row Access Policy is applied.

Question 14

A data architect is designing a pipeline that requires data freshness within 5 minutes across a series of five interdependent tables. The architect wants to minimize the operational overhead of managing task schedules and manual dependency logic. Which Snowflake feature should be prioritized to meet these requirements?

Accepted Answer

Dynamic Tables using the TARGET_LAG parameter set to 5 minutes.. Dynamic tables simplify data engineering by automating the refresh process based on a defined target lag rather than manual task orchestration. Snowflake's scheduler determines the optimal execution order to meet the freshness requirements across the entire graph. This shift from imperative to declarative pipelines reduces the risk of scheduling gaps and simplifies the management of complex dependencies.

Answer

Standard Streams and Tasks with explicit AFTER dependencies.

Answer

Materialized Views on each of the five tables with automatic clustering.

Answer

Snowpipe with a custom Lambda function to trigger downstream updates.

Question 15

Refer to the exhibit. What is the most likely performance issue here?

Accepted Answer

The table is not effectively clustered by the date column.. The exhibit shows that the query scanned all 1,000 partitions despite applying a filter on a date range. This indicates that the data is not physically organized by the date column, preventing the engine from performing partition pruning. Because no partitions could be skipped, the engine was forced to scan every single micro-partition, leading to long execution times regardless of the filter's narrow range.

Answer

The warehouse is too small for the amount of data.

Answer

The result cache is disabled.

Answer

The query is missing a search optimization index.