Snowflake ARA-C01 Practice Exam Dumps 2025

According to the SnowPro Advanced: Architect documents and learning resources, a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka is that the Connector creates and manages its own stage, file format, and pipe objects. The stage is an internal stage that is used to store the data files from the Kafka topics. The file format is a JSON or Avro file format that is used to parse the data files. The pipe is a Snowpipe object that is used to load the data files into the Snowflake table. The Connector automatically creates and configures these objects based on the Kafka configuration properties, and handles the cleanup and maintenance of these objects1.

The other options are incorrect because they are not characteristics of loading data into Snowflake using the Snowflake Connector for Kafka. Option A is incorrect because the Connector works in Snowflake regions that use any cloud infrastructure, not just AWS. The Connector supports AWS, Azure, and Google Cloud platforms, and can load data across different regions and cloud platforms using data replication2. Option B is incorrect because the Connector does not work with all file formats, only JSON and Avro. The Connector expects the data in the Kafka topics to be in JSON or Avro format, and parses the data accordingly. Other file formats, such as text, ORC, Parquet, or XML, are not supported by the Connector3. Option D is incorrect because loads using the Connector do not have lower latency than Snowpipe, and do not ingest data in real time. The Connector uses Snowpipe to load data into Snowflake, and inherits the same latency and performance characteristics of Snowpipe. The Connector does not provide real-time ingestion, but near real-time ingestion, depending on the frequency and size of the data files4. References: Installing and Configuring the Kafka Connector | Snowflake Documentation, Sharing Data Across Regions and Cloud Platforms | Snowflake Documentation, Overview of the Kafka Connector | Snowflake Documentation, Using Snowflake Connector for Kafka With Snowpipe Streaming | Snowflake Documentation

Question 2

Consider the following COPY command which is loading data with CSV format into a Snowflake table from an internal stage through a data transformation query.

This command results in the following error:

SQL compilation error: invalid parameter 'validation_mode'

Assuming the syntax is correct, what is the cause of this error?

Options:

The VALIDATION_MODE parameter supports COPY statements that load data from external stages only.

The VALIDATION_MODE parameter does not support COPY statements with CSV file formats.

The VALIDATION_MODE parameter does not support COPY statements that transform data during a load.

The value return_all_errors of the option VALIDATION_MODE is causing a compilation error.

Answer:

Explanation:

The VALIDATION_MODE parameter is used to specify the behavior of the COPY statement when loading data into a table. It is used to specify whether the COPY statement should return an error if any of the rows in the file are invalid or if it should continue loading the valid rows. The VALIDATION_MODE parameter is only supported for COPY statements that load data from external stages1.

The query in the question uses a data transformation query to load data from an internal stage. A data transformation query is a query that transforms the data during the load process, such as parsing JSON or XML data, applying functions, or joining with other tables2.

According to the documentation, VALIDATION_MODE does not support COPY statements that transform data during a load. If the parameter is specified, the COPY statement returns an error1. Therefore, option C is the correct answer.

COPY INTO

: Transforming Data During a Load

Question 3

A company is designing its serving layer for data that is in cloud storage. Multiple terabytes of the data will be used for reporting. Some data does not have a clear use case but could be useful for experimental analysis. This experimentation data changes frequently and is sometimes wiped out and replaced completely in a few days.

The company wants to centralize access control, provide a single point of connection for the end-users, and maintain data governance.

What solution meets these requirements while MINIMIZING costs, administrative effort, and development overhead?

Options:

Import the data used for reporting into a Snowflake schema with native tables. Then create external tables pointing to the cloud storage folders used for the experimentation data. Then create two different roles with grants to the different datasets to match the different user personas, and grant these roles to the corresponding users.

Import all the data in cloud storage to be used for reporting into a Snowflake schema with native tables. Then create a role that has access to this schema and manage access to the data through that role.

Import all the data in cloud storage to be used for reporting into a Snowflake schema with native tables. Then create two different roles with grants to the different datasets to match the different user personas, and grant these roles to the corresponding users.

Import the data used for reporting into a Snowflake schema with native tables. Then create views that have SELECT commands pointing to the cloud storage files for the experimentation data. Then create two different roles to match the different user personas, and grant these roles to the corresponding users.

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ARA-C01 Exam Dumps : SnowPro Advanced: Architect Certification Exam

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