2025 Realistic Verified ARA-C01 exam dumps Q&As – ARA-C01 Free Update [Q72-Q90]

Q72. Which technique will efficiently ingest and consume semi-structured data for Snowflake data lake workloads?

IDEF1X

Schema-on-write

Schema-on-read

Information schema

Q73. A company wants to deploy its Snowflake accounts inside its corporate network with no visibility on the internet. The company is using a VPN infrastructure and Virtual Desktop Infrastructure (VDI) for its Snowflake users. The company also wants to re-use the login credentials set up for the VDI to eliminate redundancy when managing logins.
What Snowflake functionality should be used to meet these requirements? (Choose two.)

Set up replication to allow users to connect from outside the company VPN.

Provision a unique company Tri-Secret Secure key.

Use private connectivity from a cloud provider.

Set up SSO for federated authentication.

Use a proxy Snowflake account outside the VPN, enabling client redirect for user logins.

According to the SnowPro Advanced: Architect documents and learning resources, the Snowflake functionality that should be used to meet these requirements are:
Use private connectivity from a cloud provider. This feature allows customers to connect to Snowflake from their own private network without exposing their data to the public Internet. Snowflake integrates with AWS PrivateLink, Azure Private Link, and Google Cloud Private Service Connect to offer private connectivity from customers’ VPCs or VNets to Snowflake endpoints. Customers can control how traffic reaches the Snowflake endpoint and avoid the need for proxies or public IP addresses123.
Set up SSO for federated authentication. This feature allows customers to use their existing identity provider (IdP) to authenticate users for SSO access to Snowflake. Snowflake supports most SAML 2.0-compliant vendors as an IdP, including Okta, Microsoft AD FS, Google G Suite, Microsoft Azure Active Directory, OneLogin, Ping Identity, and PingOne. By setting up SSO for federated authentication, customers can leverage their existing user credentials and profile information, and provide stronger security than username/password authentication4.
The other options are incorrect because they do not meet the requirements or are not feasible. Option A is incorrect because setting up replication does not allow users to connect from outside the company VPN. Replication is a feature of Snowflake that enables copying databases across accounts in different regions and cloud platforms. Replication does not affect the connectivity or visibility of the accounts5. Option B is incorrect because provisioning a unique company Tri-Secret Secure key does not affect the network or authentication requirements. Tri-Secret Secure is a feature of Snowflake that allows customers to manage their own encryption keys for data at rest in Snowflake, using a combination of three secrets: a master key, a service key, and a security password. Tri-Secret Secure provides an additional layer of security and control over the data encryption and decryption process, but it does not enable private connectivity or SSO6. Option E is incorrect because using a proxy Snowflake account outside the VPN, enabling client redirect for user logins, is not a supported or recommended way of meeting the requirements. Client redirect is a feature of Snowflake that allows customers to connect to a different Snowflake account than the one specified in the connection string. This feature is useful for scenarios such as cross-region failover, data sharing, and account migration, but it does not provide private connectivity or SSO7. Reference: AWS PrivateLink & Snowflake | Snowflake Documentation, Azure Private Link & Snowflake | Snowflake Documentation, Google Cloud Private Service Connect & Snowflake | Snowflake Documentation, Overview of Federated Authentication and SSO | Snowflake Documentation, Replicating Databases Across Multiple Accounts | Snowflake Documentation, Tri-Secret Secure | Snowflake Documentation, Redirecting Client Connections | Snowflake Documentation

Q74. What is a key consideration when setting up search optimization service for a table?

Search optimization service works best with a column that has a minimum of 100 K distinct values.

Search optimization service can significantly improve query performance on partitioned external tables.

Search optimization service can help to optimize storage usage by compressing the data into a GZIP format.

The table must be clustered with a key having multiple columns for effective search optimization.

Search optimization service is a feature of Snowflake that can significantly improve the performance of certain types of lookup and analytical queries on tables. Search optimization service creates and maintains a persistent data structure called a search access path, which keeps track of which values of the table’s columns might be found in each of its micro-partitions, allowing some micro-partitions to be skipped when scanning the table1.
Search optimization service can significantly improve query performance on partitioned external tables, which are tables that store data in external locations such as Amazon S3 or Google Cloud Storage. Partitioned external tables can leverage the search access path to prune the partitions that do not contain the relevant data, reducing the amount of data that needs to be scanned and transferred from the external location2.
The other options are not correct because:
* A. Search optimization service works best with a column that has a high cardinality, which means that the column has many distinct values. However, there is no specific minimum number of distinct values required for search optimization service to work effectively. The actual performance improvement depends on the selectivity of the queries and the distribution of the data1.
* C. Search optimization service does not help to optimize storage usage by compressing the data into a GZIP format. Search optimization service does not affect the storage format or compression of the data, which is determined by the file format options of the table. Search optimization service only creates an additional data structure that is stored separately from the table data1.
* D. The table does not need to be clustered with a key having multiple columns for effective search optimization. Clustering is a feature of Snowflake that allows ordering the data in a table or a partitioned external table based on one or more clustering keys. Clustering can improve the performance of queries that filter on the clustering keys, as it reduces the number of micro-partitions that need to be scanned. However, clustering is not required for search optimization service to work, as search optimization service can skip micro-partitions based on any column that has a search access path, regardless of the clustering key3.
References:
* 1: Search Optimization Service | Snowflake Documentation
* 2: Partitioned External Tables | Snowflake Documentation
* 3: Clustering Keys | Snowflake Documentation

Q75. Database DB1 has schema S1 which has one table, T1.
DB1 –> S1 –> T1
The retention period of EG1 is set to 10 days.
The retention period of s: is set to 20 days.
The retention period of t: Is set to 30 days.
The user runs the following command:
Drop Database DB1;
What will the Time Travel retention period be for T1?

10 days

20 days

30 days

37 days

Q76. A retail company has over 3000 stores all using the same Point of Sale (POS) system. The company wants to deliver near real-time sales results to category managers. The stores operate in a variety of time zones and exhibit a dynamic range of transactions each minute, with some stores having higher sales volumes than others.
Sales results are provided in a uniform fashion using data engineered fields that will be calculated in a complex data pipeline. Calculations include exceptions, aggregations, and scoring using external functions interfaced to scoring algorithms. The source data for aggregations has over 100M rows.
Every minute, the POS sends all sales transactions files to a cloud storage location with a naming convention that includes store numbers and timestamps to identify the set of transactions contained in the files. The files are typically less than 10MB in size.
How can the near real-time results be provided to the category managers? (Select TWO).

All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion.

A Snowpipe should be created and configured with AUTO_INGEST = true. A stream should be created to process INSERTS into a single target table using the stream metadata to inform the store number and timestamps.

A stream should be created to accumulate the near real-time data and a task should be created that runs at a frequency that matches the real-time analytics needs.

An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs.

The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement.

To provide near real-time sales results to category managers, the Architect can use the following steps:
Create an external stage that references the cloud storage location where the POS sends the sales transactions files. The external stage should use the file format and encryption settings that match the source files2 Create a Snowpipe that loads the files from the external stage into a target table in Snowflake. The Snowpipe should be configured with AUTO_INGEST = true, which means that it will automatically detect and ingest new files as they arrive in the external stage. The Snowpipe should also use a copy option to purge the files from the external stage after loading, to avoid duplicate ingestion3 Create a stream on the target table that captures the INSERTS made by the Snowpipe. The stream should include the metadata columns that provide information about the file name, path, size, and last modified time. The stream should also have a retention period that matches the real-time analytics needs4 Create a task that runs a query on the stream to process the near real-time data. The query should use the stream metadata to extract the store number and timestamps from the file name and path, and perform the calculations for exceptions, aggregations, and scoring using external functions. The query should also output the results to another table or view that can be accessed by the category managers. The task should be scheduled to run at a frequency that matches the real-time analytics needs, such as every minute or every 5 minutes.
The other options are not optimal or feasible for providing near real-time results:
All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion. This option is not recommended because it would introduce additional latency and complexity in the data pipeline. Concatenating files would require an external process or service that monitors the cloud storage location and performs the file merging operation. This would delay the ingestion of new files into Snowflake and increase the risk of data loss or corruption. Moreover, concatenating files would not avoid micro-ingestion, as Snowpipe would still ingest each concatenated file as a separate load.
An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs. This option is not necessary because Snowpipe can automatically ingest new files from the external stage without requiring an external trigger or scheduler. Using an external scheduler would add more overhead and dependency to the data pipeline, and it would not guarantee near real-time ingestion, as it would depend on the polling interval and the availability of the external scheduler.
The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement. This option is not feasible because tasks cannot be scheduled to run every second in Snowflake. The minimum interval for tasks is one minute, and even that is not guaranteed, as tasks are subject to scheduling delays and concurrency limits. Moreover, using the copy into command with a task would not leverage the benefits of Snowpipe, such as automatic file detection, load balancing, and micro-partition optimization. Reference:
1: SnowPro Advanced: Architect | Study Guide
2: Snowflake Documentation | Creating Stages
3: Snowflake Documentation | Loading Data Using Snowpipe
4: Snowflake Documentation | Using Streams and Tasks for ELT
5: Snowflake Documentation | Creating Tasks
6: Snowflake Documentation | Best Practices for Loading Data
7: Snowflake Documentation | Using the Snowpipe REST API
8: Snowflake Documentation | Scheduling Tasks
9: SnowPro Advanced: Architect | Study Guide
10: Creating Stages
11: Loading Data Using Snowpipe
12: Using Streams and Tasks for ELT
13: [Creating Tasks]
14: [Best Practices for Loading Data]
15: [Using the Snowpipe REST API]
16: [Scheduling Tasks]

Q77. A user is executing the following command sequentially within a timeframe of 10 minutes from start to finish:

What would be the output of this query?

Table T_SALES_CLONE successfully created.

Time Travel data is not available for table T_SALES.

The offset -> is not a valid clause in the clone operation.

Syntax error line 1 at position 58 unexpected ‘at’.

Q78. You can define a clustering key directly on top of VARIANT columns

TRUE

FALSE

Q79. Which of the below privileges are required for search optimization?

OWNERSHIP privilege on the table

ADD SEARCH OPTIMIZATION privilege on the schema that contains the table

ADD SEARCH OPTIMIZATION privilege on the table.

Q80. How can the Snowpipe REST API be used to keep a log of data load history?

Call insertReport every 20 minutes, fetching the last 10,000 entries.

Call loadHistoryScan every minute for the maximum time range.

Call insertReport every 8 minutes for a 10-minute time range.

Call loadHistoryScan every 10 minutes for a 15-minutes range.

Q81. A user is executing the following command sequentially within a timeframe of 10 minutes from start to finish:

What would be the output of this query?

Table T_SALES_CLONE successfully created.

Time Travel data is not available for table T_SALES.

The offset -> is not a valid clause in the clone operation.

Syntax error line 1 at position 58 unexpected ‘at’.

Q82. Files arrive in an external stage every 10 seconds from a proprietary system. The files range in size from 500 K to 3 MB. The data must be accessible by dashboards as soon as it arrives.
How can a Snowflake Architect meet this requirement with the LEAST amount of coding? (Choose two.)

Use Snowpipe with auto-ingest.

Use a COPY command with a task.

Use a materialized view on an external table.

Use the COPY INTO command.

Use a combination of a task and a stream.

Q83. A company needs to have the following features available in its Snowflake account:
1. Support for Multi-Factor Authentication (MFA)
2. A minimum of 2 months of Time Travel availability
3. Database replication in between different regions
4. Native support for JDBC and ODBC
5. Customer-managed encryption keys using Tri-Secret Secure
6. Support for Payment Card Industry Data Security Standards (PCI DSS)
In order to provide all the listed services, what is the MINIMUM Snowflake edition that should be selected during account creation?

Standard

Enterprise

Business Critical

Virtual Private Snowflake (VPS)

Q84. The kafka connector creates one pipe for each partition in a Kafka topic.

TRUE

FALSE

Q85. A new user user_01 is created within Snowflake. The following two commands are executed:
Command 1-> show grants to user user_01;
Command 2 ~> show grants on user user 01;
What inferences can be made about these commands?

Command 1 defines which user owns user_01
Command 2 defines all the grants which have been given to user_01

Command 1 defines all the grants which are given to user_01 Command 2 defines which user owns user_01

Command 1 defines which role owns user_01
Command 2 defines all the grants which have been given to user_01

Command 1 defines all the grants which are given to user_01Command 2 defines which role owns user 01

Q86. An Architect has a VPN_ACCESS_LOGS table in the SECURITY_LOGS schema containing timestamps of the connection and disconnection, username of the user, and summary statistics.
What should the Architect do to enable the Snowflake search optimization service on this table?

Assume role with OWNERSHIP on future tables and ADD SEARCH OPTIMIZATION on the SECURITY_LOGS schema.

Assume role with ALL PRIVILEGES including ADD SEARCH OPTIMIZATION in the SECURITY LOGS schema.

Assume role with OWNERSHIP on VPN_ACCESS_LOGS and ADD SEARCH OPTIMIZATION in the SECURITY_LOGS schema.

Assume role with ALL PRIVILEGES on VPN_ACCESS_LOGS and ADD SEARCH OPTIMIZATION in the SECURITY_LOGS schema.

Q87. 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?

C5, C4, C2

C3, C4, C5

C1, C3, C2

C2, C1, C3

Q88. Which alter command below may affect the availability of column with respect to time travel?

ALTER TABLE…DROP COLUMN

ALTER TABLE…SET DATA TYPE

ALTER TABLE…SET DEFAULT

Q89. Company A would like to share data in Snowflake with Company B.
Company B is not on the same cloud platform as Company A.
What is required to allow data sharing between these two companies?

Create a pipeline to write shared data to a cloud storage location in the target cloud provider.

Ensure that all views are persisted, as views cannot be shared across cloud platforms.

Setup data replication to the region and cloud platform where the consumer resides.

Company A and Company B must agree to use a single cloud platform: Data sharing is only possible if the companies share the same cloud provider.

Q90. Refreshing a secondary database is not allowed in the following circumstances

Materialized views

Primary database contains transient tables

Databases created from shares

Primary database has external table

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