GES-C01 exam material & Free GES-C01 AI Tutor

QUESTION: 17

What authentication methods are supported when accessing the Snowflake Cortex LLM REST API? (Choose two.)

SAML 2.0
OAuth 2.0
Single Sign-On (SSO)
Key-pair authentication
Multi-Factor Authentication (MFA)

Answer(s): B,D

Explanation:

The Snowflake Cortex LLM REST API follows the same authentication model as Snowflake’s native APIs and supports OAuth 2.0 and key-pair (asymmetric) authentication .
OAuth 2.0 is the recommended method for user-driven access; it allows a client to obtain a short-lived access token by exchanging client-id/secret or via the JWT-bearer flow, which can be scoped to the CORTEX role.
Key-pair authentication (asymmetric JWT signing with a private key) enables service-to-service or automated scripts to call the endpoint without interactive user interaction, and it is also listed as a supported method in the Snowflake documentation.
The other options are not applicable:
SAML 2.0 and SSO are enterprise-wide identity mechanisms used for web-based applications, but they are not directly exposed as authentication schemes for the Cortex LLM REST endpoints. Multi-Factor Authentication (MFA) is a security attribute enforced by Snowflake’s security policies, but it is not an API authentication protocol itself; the API does not issue separate MFA credentials.
Therefore, the two supported methods are OAuth 2.0 and Key-pair authentication (options B and D ).

Reference:

Snowflake Cortex LLM REST API Authentication Overview – Snowflake Documentation https://docs.snowflake.com/en/developer-guide/snowflake-cortex/rest-api-auth
OAuth 2.0 and Key-Pair Authentication for Snowflake Cortex – Snowflake Community https://community.snowflake.com/s/article/OAuth-2-0-and-Key-Pair-Authentication-for-Cortex-LLM-REST-API

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QUESTION: 18

This use case sources auto part details from two data systems:

Which query will compare the part descriptions between the two data sources?

Answer(s): D

Explanation:

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QUESTION: 19

A Gen AI Specialist is designing a Gen AI-driven data pipeline in Snowflake to meet these requirements: Download PDFs from a website once an hour Load the PDFs into an internal stage Extract text from the PDFs and load the text into a Snowflake table Use prompt engineering to generate an opinion on the text based on a given persona Automate the pipeline to maximize efficiency What Snowflake features will be required to create this data pipeline using stored procedures? (Choose three.)

Snowpipe
Streams and tasks
External access integration
Cortex SENTIMENT function
Cortex TRY_COMPLETE function
Dynamic tables using incremental refreshes

Answer(s): B,C,E

Explanation:

Why BCE is the right choice
B – Streams and tasks – Streams capture new files as they land in the stage, and tasks schedule the stored-procedure workflow (download  →  load  →  transform) on an hourly cadence, ensuring a fully automated, repeatable pipeline. C – External access integration – Allows a stored procedure to invoke an HTTP endpoint directly from Snowflake, enabling the “download PDFs from a website once an hour” step without moving data out of Snowflake. E – Cortex TRY_COMPLETE – Generates natural-language output based on a prompt that includes the extracted text and a persona instruction; this is the only Cortex function that can produce custom opinions, making it essential for the persona-driven opinion step.
Why the other options are not suitable
A – Snowpipe – Designed for continuous bulk ingestion from external stages; here the ingestion is driven by a scheduled HTTP fetch and can be handled by tasks, so Snowpipe adds unnecessary complexity. D – Cortex SENTIMENT – Provides only sentiment scores, which are not tailored to a persona-based opinion;
the requirement calls for a generative response, best served by TRY_COMPLETE. F – Dynamic tables using incremental refreshes – Dynamic tables are useful for materialized views that change frequently, but the pipeline described is orchestrated through stored procedures and tasks; dynamic tables are not required to meet the hourly automation and opinion-generation goals.

Reference:

1. Snowflake Documentation – Creating and Using Streams and Tasks https://docs.snowflake.com/en/user-guide/data-streams-tasks
2. Snowflake Documentation – External Functions and External Access Integrations https://docs.snowflake.com/en/sql-reference/external-function
3. Snowflake Documentation – Cortex: TRY_COMPLETE
https://docs.snowflake.com/en/cortex/cortex-try_complete
4. Snowflake Documentation – Dynamic Tables Overview https://docs.snowflake.com/en/sql-reference/sql/create-dynamic-table

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QUESTION: 20

A Gen AI Specialist uploaded a PDF document named Timesheet_report.pdf to a Snowflake stage. The PDF contains a table with this structure:

The Specialist then executes a command to extract the content from the document, formatted as Markdown, to preserve the table structure: The Specialist then executes a command to extract the content from the document, formatted as Markdown, to preserve the table structure:

What will be the output of this command?

Answer(s): B

Explanation:

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QUESTION: 21

Which arguments are required when using the log_model method to log a model in the Snowflake Model Registry? (Choose two.)

model
model_name
code_paths
version_name
python_version

Answer(s): A,B

Explanation:

Technical justification model (A) – The model argument supplies the actual trained artefact (e.g., a PyTorch or TensorFlow object) that
Snowflake needs to store in the Model Registry. Without providing the model object the function has nothing to register, so this argument is mandatory. model_name (B) – The model_name argument uniquely identifies the target model within the registry namespace. Snowflake requires a name to create or update a model entry; omitting it would leave the registry without a reference point, making this argument mandatory.
The remaining arguments are optional:
version_name (D) can be omitted; if not supplied Snowflake auto-generates a sequential version.
code_paths (C) are only needed when you want to register external source files alongside the model, which is not required for basic logging. python_version (E) merely informs Snowflake of the runtime version and does not affect the registration process, so it is not required.
Thus, the only required arguments are A and B .

Reference:

Snowflake Documentation – Log a model in Snowflake Model Registry: https://docs.snowflake.com/en/developer-guide/snowpark-python/machine-learning/log-model Snowflake Documentation – Snowpark Python API Reference – log_model: https://docs.snowflake.com/en/developer-guide/snowpark-python/reference/ml_functions#log_model

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QUESTION: 22

A Gen AI Specialist deployed a base model as an LLM in a production environment as a proof of concept. It is larger and more costly than other base models. How can the Specialist reduce costs in production, without impacting the performance of the limited tasks required to support the proof of concept?

Use Snowflake Cortex Search to limit the number of context documents.
Use Snowflake Cortex Agents to ensure the base model is concise
Use Snowflake Cortex Fine-tuning on a different base modal
Provide a semantic model to ensure the LLM is able to interpret the prompts

Answer(s): C

Explanation:

Technical Justification
Deploying a smaller, task-specific variant of the base model through Snowflake Cortex Fine-tuning reduces the model’s parameter count and computational footprint, directly lowering cost per inference while preserving accuracy on the narrowly defined proof-of-concept workloads. Tailoring the model to the specific semantics of the target tasks eliminates unnecessary processing of generic knowledge, which means fewer tokens are needed for inference and storage costs drop accordingly. Approaches that merely restrict context length or rely on prompting tricks do not alter the underlying resource consumption of the original large model; they only affect input size and therefore cannot achieve the same level of cost reduction. Introducing a semantic layer or using conversational agents may improve interaction quality but still executes the same high-capacity model under the hood, so operational expenses remain unchanged. Consequently, fine-tuning the model to a more compact, domain-relevant version is the only technique that simultaneously cuts production costs and maintains the required performance level.

Reference:

Snowflake Cortex Overview: https://docs.snowflake.com/en/snowflake-cortex Fine-tuning Models in Snowflake Cortex: https://docs.snowflake.com/en/snowflake-cortex/finetuning

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QUESTION: 23

Which components are required to deploy and run a container using Snowpark Container Services? (Choose two.)

A compute pool to run the container
An external stage linked to Amazon S3
A schedule task assigned to a virtual warehouse
A file format registered for JSON or CSV data ingestion
A service specification that defines the container behavior

Answer(s): A,E

Explanation:

Technical justification
A – Compute pool is mandatory because Snowpark Container Services (S-CS) runs the container on a Snowflake compute pool (i.e., a set of Snowflake virtual warehouses). The pool provides the compute resources and scaling behavior required for the container to start, execute, and be monitored. E – Service specification is required to define the runtime behavior of the container service. It contains the image location, startup command, exposed ports, resource limits, and other configuration options that tell Snowflake how to launch and manage the container.
These two items (A and E) are the core building blocks needed to deploy and run a container on S-CS. Without a pool you have no compute to host the container; without a service spec Snowflake does not know how to treat the container image or what expectations to enforce.
Why the other options are not required for deployment/runtime
B – External stage linked to Amazon S3 is only needed when you load data from external files; it is not part of the container-deployment workflow in S-CS. C – Schedule task assigned to a virtual warehouse is related to scheduled data loading jobs, not to the execution of a container service. D – File format registered for JSON or CSV ingestion applies to the Snowpipe ingestion pipeline, not to container execution.
Thus, only A and E satisfy the requirements for provisioning a container with Snowpark Container Services.

Reference:

Snowflake Documentation – Deploying Containers with Snowpark Container Services : https://docs.snowflake.com/en/snowpark-container-services/deploying-containers Snowflake Documentation – Service Specification for Snowpark Container Services : https://docs.snowflake.com/en/snowpark-container-services/service-specification

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QUESTION: 24

How are usage and costs calculated for Snowflake Cortex Guard?

Compute charges are based on the number of rows scanned in a table.
Compute charges are based on the number of input tokens processed.
Charges are based on the number of users accessing the date.
Charges are based on the total number of billable tokens processed when queries use data in the from_text and question fields.

Answer(s): D

Explanation:

Why option D is correct
Snowflake Cortex Guard is billed for the billable tokens that are actually consumed when a query accesses the protected fields ( from_text and question ).
Each token processed contributes to the compute cost; there is no per-row, per-user, or flat-rate charge. The pricing model therefore aligns usage-based billing with the amount of textual data that Cortex Guard analyzes.
Why the other options are unsuitable

A: “Compute charges are based on the number of rows scanned in a table.” Snowflake’s elastic compute is token-centric for Cortex Guard, not row-count dependent, so row scans do not drive the cost.
B: “Compute charges are based on the number of input tokens processed.” This statement is partially true but incomplete; the cost is tied specifically to tokens in the from_text and question fields, not to all input tokens of an arbitrary query.
C: “Charges are based on the number of users accessing the data.” User count does not affect Cortex Guard pricing; charges are purely data-processing (token) based.

Reference:

Snowflake Cortex Guard Overview & Pricing: https://docs.snowflake.com/en/cortex-guide/guard-intro Cortex Guard Billing Details (Pricing model): https://docs.snowflake.com/en/cortex-guide/cortex-guard-pricing

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DAX Transaction Category = CALCULATE( MAX('Transaction Size'[Size]), FILTER( 'Transaction Size', AND( Sales[Sales Amount] >= 'Transaction Size'[Minimum], Sales[Sales Amount] <= 'Transaction Size'[Maximum] ) ) )

Snowflake GES-C01: Skills Tested, Job Roles, and Study Tips

The SnowPro Specialty Gen AI certification is designed for data professionals, architects, and engineers who work directly with generative artificial intelligence workloads within the Snowflake ecosystem. Organizations hiring for this role typically look for individuals who can design, implement, and manage AI-driven solutions that leverage Snowflake as the primary data platform. This certification confirms that a professional possesses the technical depth required to integrate large language models and document processing capabilities into existing data pipelines. By validating these specific skills, the certification helps employers identify candidates capable of maintaining secure and performant AI architectures. It serves as a benchmark for those responsible for the deployment of intelligent applications that rely on the Snowflake Data Cloud.

What the GES-C01 Exam Covers

The exam evaluates a candidate's ability to navigate the Snowflake for GenAI overview, which establishes the foundational knowledge of how the platform supports modern AI workflows. Candidates must demonstrate proficiency in utilizing Snowflake GenAI and LLM functions to perform complex data tasks, such as text analysis and generation, directly within the platform. Furthermore, the exam tests the candidate's understanding of Snowflake GenAI governance, ensuring that they can implement necessary security and compliance controls for sensitive data used in AI models. Proficiency in Snowflake Document AI is also required, as this area focuses on extracting insights from unstructured documents using the platform's native capabilities. Our practice questions are designed to cover these domains comprehensively, allowing candidates to test their knowledge across all required technical areas.

The most technically demanding aspect of the exam involves the integration of Snowflake GenAI governance with LLM functions. Candidates must understand how to apply role-based access control and data masking to ensure that AI models do not expose restricted information during inference. This requires a deep understanding of how Snowflake manages data security at the object level while simultaneously providing the compute power necessary for generative tasks. Mastering this intersection is essential for passing the exam, as many scenario-based questions focus on balancing model performance with strict organizational security requirements.

Are These Real GES-C01 Exam Questions?

Our platform provides practice questions that reflect what appears on the real exam because they are sourced from the community of professionals who have recently completed the certification. These community-verified resources are designed to help you understand the logic behind the exam rather than simply memorizing patterns. If you have been searching for GES-C01 exam dumps or braindump files, our community-verified practice questions offer something more valuable, as each question is verified and explained by IT professionals who recently passed the exam. We do not provide leaked or confidential content, as our focus remains on helping candidates master the underlying concepts required for success. By using these materials, you gain access to a repository of knowledge that is constantly updated based on the experiences of those who have sat for the actual test.

Community verification works through a collaborative process where users actively discuss the reasoning behind each answer choice. When a question is flagged or debated, experienced members of the community provide context from their own exam experience to clarify the correct approach. This peer-reviewed environment ensures that the explanations remain accurate and aligned with the current Snowflake documentation. This collective effort provides a level of reliability that static study guides cannot match, as it incorporates real-world feedback from the field.

How to Prepare for the GES-C01 Exam

Effective exam preparation requires a combination of hands-on experience in a Snowflake environment and a thorough review of official documentation. Candidates should focus on building small-scale projects that utilize Document AI and LLM functions to gain practical familiarity with the syntax and configuration requirements. Every practice question includes a free AI Tutor explanation that breaks down the reasoning behind the correct answer, so you understand the concept, not just the answer. We recommend creating a structured study schedule that allocates specific time for each of the four main exam topics. Utilizing the AI Tutor as a supplemental study partner allows you to clarify complex topics and reinforce your understanding of how Snowflake handles generative AI workloads.

A common mistake candidates make is relying on rote memorization of questions instead of focusing on the underlying technical principles. Because the certification exam often presents scenario-based questions, you must be able to apply your knowledge to unique situations rather than recalling specific answers. Time management is another critical factor, as candidates often struggle to complete the exam within the allotted time if they are not comfortable with the interface and the complexity of the questions. To avoid these pitfalls, practice under timed conditions and prioritize understanding the "why" behind each configuration choice.

What to Expect on Exam Day

On the day of your Snowflake certification exam, you should expect a series of questions that test your ability to apply technical knowledge to real-world scenarios. The format typically includes multiple-choice questions, which may require selecting one or more correct answers based on the provided technical context. The exam is administered through a secure testing environment, often via Pearson VUE, which ensures the integrity of the certification process. You will have a set amount of time to complete the assessment, and it is important to pace yourself carefully to ensure you have enough time to review your answers. Familiarizing yourself with the exam interface beforehand can help reduce anxiety and allow you to focus entirely on the technical content.

Who Should Use These GES-C01 Practice Questions

These practice questions are intended for data engineers, AI developers, and cloud architects who have experience with the Snowflake platform and are looking to validate their expertise in generative AI. Typically, candidates should have several months of hands-on experience with Snowflake features to ensure they can handle the practical nature of the certification exam. By engaging in consistent exam preparation, you can identify your knowledge gaps and focus your efforts on the areas that require the most improvement. Passing this certification exam serves as a professional milestone that demonstrates your ability to lead AI initiatives within a data-driven organization. It is an essential step for those aiming to advance their career in the rapidly growing field of AI-integrated data management.

To get the most out of these practice questions, do not simply read the answer and move on to the next item. Engage deeply with the AI Tutor explanation to ensure you grasp the core concepts, and participate in the community discussions to see how others approach the same problems. If you find yourself consistently missing questions in a specific domain, revisit the official Snowflake documentation before attempting those questions again. Browse the questions above and use the community discussions and AI Tutor to build real exam confidence.

Updated on: 16 July, 2026

Snowflake GES-C01 Exam Prep
SnowPro Specialty Gen AI (Page 4 )

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Two-part tariff pricing

Dual pricing

Snowflake GES-C01: Skills Tested, Job Roles, and Study Tips

What the GES-C01 Exam Covers

Are These Real GES-C01 Exam Questions?

How to Prepare for the GES-C01 Exam

What to Expect on Exam Day

Who Should Use These GES-C01 Practice Questions

Snowflake GES-C01 Exam Prep SnowPro Specialty Gen AI (Page 4 )

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QUESTION: 23

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QUESTION: 24

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Reference:

Two-part tariff pricing

Dual pricing

Snowflake GES-C01: Skills Tested, Job Roles, and Study Tips

What the GES-C01 Exam Covers

Are These Real GES-C01 Exam Questions?

How to Prepare for the GES-C01 Exam

What to Expect on Exam Day

Who Should Use These GES-C01 Practice Questions

Snowflake GES-C01 Exam Prep
SnowPro Specialty Gen AI (Page 4 )