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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q168-Q173):

NEW QUESTION # 168
A development team plans to utilize Snowpark Container Services (SPCS) for deploying a variety of AI/ML workloads, including custom LLMs and GPU-accelerated model training jobs. They are in the process of creating a compute pool and need to select the appropriate instance families and configurations. Which of the following statements about 'CREATE COMPUTE POOL' in SPCS are accurate?

A. Snowpark-optimized warehouses are the recommended compute pool type for all large-scale ML training workloads within SPCS due to their enhanced memory limits and CPU architectures.
B. The 'MIN NODES' and 'MAX NODES parameters define the scaling range for the compute pool, and Snowflake automatically scales the pool within this range based on workload demand.
C. Setting 'AUTO RESUME = TRUE ensures that the compute pool automatically starts when a service or job is submitted to it, rather than requiring manual resumption.
D. For cost optimization, 'AUTO SUSPEND SECS = 0' should be used to prevent automatic suspension of the compute pool, as suspension and resumption incur minimum billing durations.
E. To support GPU-accelerated LLM inference and training, the 'INSTANCE_FAMILY' must be selected from a type starting with 'GPU' (e.g.,

Answer: C,E

Explanation:
Option A is correct. GPU-accelerated workloads, such as LLM inference and model training, require instance families specifically designed with GPUs. The documentation lists instance family names starting with 'GPU' for this purpose, such as or 'GPU_GCP NV L4 Option B is incorrect. While 'MIN NODES and 'MAX NODES define the range, the size of compute clusters in Snowpark Container Services does ''not'' auto-scale dynamically based on workload demand. Users must manually alter the number of instances at runtime using commands like 'ALTER SERVICE MIN INSTANCES = s. Snowflake does handle load balancing across instances within the configured node counts. Option C is correct. The 'AUTO_RESUME = TRUE parameter, when specified during compute pool creation, enables the pool to automatically resume operation when a service or job is submitted, removing the need for explicit SALTER COMPUTE POOL RESUME commands. option D is incorrect. Setting = prevents the compute pool from automatically suspending, meaning it will continue to consume credits even when idle. This would generally lead to higher costs, not cost optimization, unless the pool is constantly active. The default is 3600 seconds (1 hour). SPCS Compute Nodes have a minimum charge of five minutes when started or resumed, making intelligent use of auto-suspend important for cost management. Option E is incorrect. Snowpark-optimized warehouses are a type of 'virtual warehouse' and are recommended for Snowpark workloads with large memory requirements or specific CPU architecture, typically for single-node ML training workloads 'within a warehouse'. SPCS compute pools, however, provide their own dedicated instance families (CPU, HighMemory, GPU) for containerized workloads, abstracting the underlying infrastructure and supporting distributed GPU clusters directly within SPCS, not Snowpark-optimized warehouses as a 'compute pool type' for SPCS.

NEW QUESTION # 169
A data engineering team is setting up a new Cortex Search Service named to power a RAG application over their table, which stores historical ticket text and metadata. They need to ensure proper setup, cost efficiency, and data integrity. Which of the following statements are true regarding the creation and initial configuration of this Cortex Search Service? (Select all that apply)

A. The role used to create the Cortex Search Service must be granted the 'SNOWFLAKE.CORTEX_USER database role.
B. The 'CREATE CORTEX SEARCH SERVICE command should specify a Snowpark-optimized warehouse for optimal performance, as it is designed for memory-intensive ML workloads.
C. To enable continuous updates of the search index as new tickets are added,
D. If the service is created using the Snowsight AI & ML Studio, its name will be double-quoted, and thus must be double-quoted when referenced in subsequent SQL queries.
E. Columns intended to be filterable in search queries must be explicitly listed in the 'ATTRIBUTES' field during service creation and must also be included in the source query for the service.

Answer: A,C,D,E

Explanation:
Option A is incorrect. Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for a Cortex Search Service, as larger warehouses do not necessarily increase performance for these functions. Snowpark-optimized warehouses are primarily for ML training workloads with large memory requirements. Option B is correct. Change tracking is required on the base table to allow continuous updates of the search service, especially if the role creating the service does not own the table. Option C is correct. The role used to create a Cortex Search Service must be granted the "SNOWFLAKE.CORTEX_USER database role. Option D is correct. Any columns specified in the 'ATTRIBUTES' field for filtering must also be included in the source query that defines the search service. Option E is correct. When a Cortex Search Service is created from Snowsight, its name is double-quoted, meaning it must be referenced using double-quotes in SQL queries.

NEW QUESTION # 170
A data engineer is developing an AI-infused data pipeline in Snowflake Notebooks to analyze Federal Reserve Meeting Minutes and official Statements, which are initially in PDF format. The goal is to determine the FED's stance on interest rates (hawkish, dovish, or neutral) and the reasoning for each ingested PDF using an LLM. The pipeline needs to automate data ingestion, text extraction, LLM inference, and store the results in a Snowflake table. Which sequence of operations and Snowflake features is most appropriate for building this pipeline within Snowflake?

A. Scrape data from an external website directly into a Snowflake table using an 'EXTERNAL FUNCTION'. Then, apply 'SNOWFLAKE.CORTEX.EXTRACT ANSWER with a question like 'What is the FED's stance?' and 'SNOWFLAKE.CORTEX.SUMMARIZE' for reasoning to enrich the table. Automate this using 'STREAMS' and 'TASKS.
B. Load unstructured PDF files into an internal stage. Use a stored procedure to download new PDFs from the FOMC website. Leverage Snowpark Container Services to deploy a fine-tuned open-source LLM (e.g., Llama 2) for text extraction and sentiment analysis, and orchestrate the pipeline with ' Dynamic TableS for continuous updates.
C. Scrape PDF data from an external website, load unstructured PDF files to an internal stage, then use a 'UDE to parse raw text from PDFs and a separate UDF' ('GENERATE_PROMPT) to encapsulate a custom prompt. Finally, use a 'TASK' to automate the process, calling Snowflake's function with the custom prompt at the point of ingestion to generate the sentiment signal and reasoning.
D. Directly ingest PDF documents into a 'VARIANT column in a Snowflake table. Then, use the SQL function in 'OCR mode to extract text and layout. The extracted text is then passed to 'SNOWFLAKE.CORTEX.CLASSIFY TEXT to determine the sentiment, and the results are stored in a new table.
E. Ingest PDF documents into a directory table. Use 'Document AI' C!PREDICT') to extract specific entities and tables from the PDFs into structured JSON. Then, create a 'STREAM' on the stage and a 'TASK' to continuously process new documents, extracting information and potentially performing additional sentiment analysis with another LLM.

Answer: C,E

Explanation:
Option A is correct. This option directly aligns with the 'AI-Infused Pipelines with Snowflake Cortex' blog post. It describes scraping data, loading to an internal stage, using a stored procedure to download new PDFs, and 'UDFS for parsing text and generating prompts. It explicitly mentions using 'Snowflake's TRY_COMPLETE function for LLM inference with a custom prompt at ingestion, all automated with 'Streams' and 'Tasks'. Option B is incorrect. PARSE DOCUMENT in 'OCR mode extracts text but does not preserve layout and primarily focuses on text and layout extraction, not directly sentiment analysis or complex reasoning. While 'CLASSIFY _ TEXT (or can classify text, 'AI PARSE DOCUMENT is for extracting text and layout from documents, not direct ingestion into a 'VARIANT' column for text extraction via 'OCR mode. is a Cortex AI SQL function, typically used on files stored in stages. Option C is incorrect. While Snowpark Container Services can host LLMs and Dynamic Tables can automate updates, using an external LLM for text extraction when 'Document AI' or are available for native PDF processing is not the most direct approach. Additionally, 'Dynamic Tables' do not support incremental refresh with 'COMPLETE , and the prompt generation and sentiment analysis would still need to be explicitly defined. Option D is incorrect. Directly scraping into a Snowflake table with an 'EXTERNAL FUNCTION' isn't covered as the primary ingestion method for PDFs in the context of LLMs for sentiment. 'EXTRACT ANSWER and 'SUMMARIZE are task-specific Cortex functions, but the core task is a multi-step pipeline for PDF content analysis, which would be better served by a robust document processing solution. The focus of the pipeline example is on PDFs, not general website scraping directly to a table for immediate LLM application. Also, 'EXTRACT_ANSWER is for extracting a specific answer, while the sentiment is a classification. Option E is correct. This option uses 'Document AI' , which is specifically designed to extract structured information (entities, tables) from unstructured documents like PDFs using 'Arctic-TILT'. It explicitly mentions creating a 'STREAM' on a stage and a "TASK' for continuous processing, which is a standard pattern for Document AI pipelines. This approach directly handles the PDF extraction and structuring into JSON, which can then be further processed for sentiment analysis or reasoning if needed. The output JSON from '!PREDICT includes various extracted fields and can be parsed.

NEW QUESTION # 171
A security audit is being conducted for a financial institution using Snowflake Cortex. Which of the following statements accurately describe Snowflake's data safety and security guarantees concerning whether customer data, metadata, or prompts leave Snowflake's governance boundary to a third-party when using Cortex features, under the default Snowflake configurations for Cortex functions unless otherwise specified?

A. When CORTEX_ENABLED_CROSS_REGION is active for Cortex LLM functions, user inputs and outputs are always cached in the intermediate region to reduce latency, thereby leaving the primary region's immediate governance.
B. For Cortex Analyst, if the legacy ENABLE_CORTEX_ANALYST_MODEL_AZURE_OPENAI account parameter is set to TRUE, customer metadata and prompts are transmitted to Azure OpenAI, but the underlying customer data is not.
C. Models brought into Snowflake via Snowpark Container Services (BYOM) are treated as Snowflake's proprietary models, meaning Snowflake assumes responsibility for their data handling policies.
D. Customer Data and inputs to Snowflake AI Features are never used by Snowflake to train or fine-tune models made available to other customers.
E. When using SNOWFLAKE .CORTEX. COMPLETE with Snowflake-hosted LLMs like all prompts and generated responses remain within Snowflake's mistral-large2, governance boundary by default.

Answer: B,D,E

Explanation:
Option A is correct because Snowflake explicitly states that Usage and Customer Data (including inputs and outputs) are NOT used to train, re-train, or fine-tune Models made available to others, and fine-tuned Models are available exclusively for the customer's use. Option B is correct as all models powering Snowflake Cortex AI functions are fully hosted in Snowflake, ensuring performance, scalability, and governance while keeping customer data secure and in place within Snowflake's governance boundary. Option C is correct as this describes a specific, legacy exception for Cortex Analyst: if the 'ENABLE CORTEX ANALYST MODEL_AZURE OPENAI' parameter is 'TRUE', then *only metadata and prompts* are transmitted outside of Snowflake's governance boundary to Microsoft Azure (a third party), while Customer Data itself is not shared. Option D is incorrect because models brought into the Service account (BYOM), for example via Snowpark Container Services, are treated as Customer Data, not Snowflake's proprietary models, and are subject to the customer's own rights and obligations as per their Customer Agreement. Option E is incorrect because when 'CORTEX ENABLED_CROSS REGION' is enabled, user inputs, service generated prompts, and outputs are explicitly *not stored or cached* during cross-region inference.

NEW QUESTION # 172
An enterprise is deploying a Cortex Analyst application and needs to manage its cost, ensure data security, and understand its operational behavior within Snowflake. Which of the following statements are true regarding the deployment, cost, and security of Cortex Analyst and its semantic models?

A. The primary cost incurred for Cortex Analyst is based on the number of tokens processed by the underlying LLMs, with more complex natural language questions directly leading to higher token usage and charges.
B. Snowflake strongly recommends enabling the ENABLE_CORTEX_ANALYST_MODEL_AZURE_OPENAI account parameter to leverage Azure OpenAI models for Cortex Analyst, as it offers the highest performance and respects RBAC restrictions for these models.
C. When using Snowflake-hosted LLMs (e.g., from Mistral or Meta) with Cortex Analyst, all customer data, including metadata and prompts, remains within Snowflake's governance boundary.
D. Administrators can monitor Cortex Analyst requests, including the user, question asked, generated SQL, and errors, by querying the SNOWFLAKLOCAL .CORTEX_ANALYST_REQUESTS function.
E. Semantic models for Cortex Analyst, whether stored as YAML files or native semantic views, should have their access controlled by RBAC. This implicitly controls access to the underlying tables referenced in the semantic model.

Answer: C,D

Explanation:
Option C is correct. By default, Cortex Analyst is powered by Snowflake-hosted LLMs from Mistral and Meta, ensuring that all data, including metadata and prompts, remains within Snowflake's governance boundary. Option E is correct. Cortex Analyst logs requests to an event table, which administrators can query using 'SNOWFLAKE.LOCAL.CORTEX_ANALYST_REQUESTS' to view details such as the user, question, generated SQL, and any errors or warnings, by specifying the semantic model type and name. Option A is incorrect. While stage access for YAML files and semantic views are controlled by RBAC, roles granted access must 'also' have 'SELECT access on all tables referenced in the semantic models; stage/view access alone does not implicitly grant table access to underlying data. Option B is incorrect. Credit usage for Cortex Analyst is based on the number of messages processed (67 Credits per 1 ,000 messages), not the number of tokens in each message. Option D is incorrect. Snowflake strongly discourages the use of the 'ENABLE_CORTEX_ANALYST_MODEL_AZURE_OPENAC parameter and advises migration to Snowflake-hosted OpenAI models. Furthermore, when this parameter is enabled, Azure OpenAI models do not respect RBAC restrictions.

NEW QUESTION # 173
......

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Free PDF Quiz Snowflake - GES-C01 Fantastic Guaranteed Success

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q168-Q173):

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