Free Snowflake SOL-C01 Exam Questions and Answer from Training Expert PrepAwayETE
Top Snowflake SOL-C01 Courses Online
NEW QUESTION # 48
How does Snowflake process queries?
- A. Using MPP compute clusters
- B. Through third-party connectors
- C. With shared-disk architecture
- D. By optimizing data in cloud storage
Answer: A
Explanation:
Snowflake processes queries usingMassively Parallel Processing (MPP)compute clusters, deployed as virtual warehouses. Each warehouse consists of multiple compute nodes working in parallel to execute queries efficiently. When a query is submitted, Snowflake distributes tasks across nodes, processes data subsets concurrently, and aggregates results. This architecture enables high performance, scalability, and the ability to handle complex analytical workloads. While Snowflake does incorporate elements of shared-disk storage, query execution itself depends on MPP compute clusters. Options such as third-party connectors or storage optimization do not represent the core query processing mechanism.
NEW QUESTION # 49
What are characteristics of the Snowflake Platform? (Select TWO).
- A. Snowflake can run on private infrastructures on-premises.
- B. Snowflake supports column-level security not row-level security.
- C. Snowflake is responsible for all data security.
- D. There is no infrastructure to provision and maintain.
- E. Snowflake handles platform maintenance, management, and upgrades.
Answer: D,E
Explanation:
Snowflake is a fully managed, cloud-native platform that automates nearly all infrastructure tasks. Two defining characteristics are:
1. Snowflake handles platform maintenance, management, and upgrades.
Snowflake automatically performs software updates, scaling operations, tuning, optimization, and patching.
Users never need to upgrade hardware, install software, or manage clusters.
2. There is no infrastructure for customers to provision and maintain.
All compute, storage, and services run on public clouds (AWS, Azure, GCP). Snowflake abstracts the underlying infrastructure completely, eliminating the need for customers to manage VMs, disks, clusters, or networking.
Incorrect options:
* Snowflake supportsbothcolumn-level security (via masking policies)androw-level security (via row access policies).
* Snowflake is not responsible foralldata security-customers must configure RBAC, masking, and governance.
* Snowflake doesnotrun on-premises; it is exclusively cloud-based.
Thus, options C and D correctly describe Snowflake platform characteristics.
NEW QUESTION # 50
Which of the following are examples of unstructured data? (Choose any 3 options)
- A. Video
- B. JSON
- C. Image
- D. Pdf
Answer: A,C,D
Explanation:
Unstructured data refers to content that lacks a predefined schema or consistent structural pattern. It cannot be represented naturally in tabular or hierarchical formats without extraction or transformation. Snowflake supports unstructured data through external tables and native features allowing storage and machine learning analyses.Images,videos, andPDFsfall into this category because they contain raw multimedia or document content without intrinsic row/column structure, often requiring OCR, video/audio processing, or content extraction. In contrast, JSON is semi-structured, containing keys, hierarchical structures, and metadata that Snowflake's VARIANT type can interpret directly. Unstructured data is becoming increasingly important for AI-driven workloads, enabling use cases such as OCR with PARSE_DOCUMENT, deep-learning analysis of images and video, and document intelligence tasks.
NEW QUESTION # 51
In Snowflake Notebooks, you execute a cell containing a SQL query that retrieves data from a view based on multiple underlying tables.
The execution status shows 'Success', but the output displays stale data. You are certain that the underlying tables have been updated recently. Which of the following steps are MOST likely to resolve this issue?
- A. Suspend and resume the Snowflake warehouse associated with the Notebook to clear any cached data.
- B. Check the definition of the view to ensure it is not using a 'SECURE VIEW' without proper privileges or a time travel point.
- C. Drop and recreate the view to ensure it reflects the latest data from the underlying tables.
- D. Execute 'ALTER VIEW REBUILD to force a refresh of the view's metadata and data.
- E. Refresh the Snowflake Notebook session by disconnecting and reconnecting.
Answer: A,B
Explanation:
Stale data despite successful execution suggests caching or privilege issues. Suspending and resuming the warehouse (C) clears potential caches. Secure views (D) might require specific privileges if the user running the notebook lacks access to the underlying data at the time the view was created, or may be based on data at a specific time travel point. Refreshing the session (A) or rebuilding the view (E) may not always guarantee a refresh of cached results. Dropping and recreating a view (B) is a drastic measure and should be considered only if other options fail.
NEW QUESTION # 52
How can a directory table be enabled in Snowflake?
- A. By setting a table policy
- B. By uploading files directly to a schema
- C. By using the COPY INTO command
- D. By using the CREATE DIRECTORY command
Answer: D
NEW QUESTION # 53
What SQL command will return a list of all files in a stage?
- A. PUT
- B. GET
- C. LIST
- D. COPY INTO
Answer: C
Explanation:
LIST @stage_name displays all files present in a stage (internal or external).
PUT uploads local files to an internal stage.
GET downloads files from an internal stage.
COPY INTO loads or unloads data but does not list files.
NEW QUESTION # 54
A data engineer is tasked with migrating a legacy data warehouse to Snowflake. They need to create a database named 'SALES DATA and a schema named 'TRANSACTIONS within it. The schema should be managed by a specific role, DATA ADMIN'. Furthermore, they need to ensure that all newly created tables within this schema are automatically granted SELECT privileges to the 'REPORTING ROLE. Which of the following steps are required to accomplish this, following Snowflake best practices for security and governance?
- A. Create the database and schema. Then, use a future grant: 'GRANT SELECT ON FUTURE TABLES IN SCHEMA SALES_DATA.TRANSACTIONS TO ROLE REPORTING ROLE;'
- B. Create the database and schema with 'OWNER = DATA_ADMIN'. Then, create a stored procedure owned by 'DATA_ADMIN' that grants 'SELECT privileges to 'REPORTING ROLE every time a new table is created in the schema.
- C. Create the database and schema with 'OWNER = DATA ADMIN'. Use 'GRANT OWNERSHIP ON SCHEMA SALES DATA.TRANSACTIONS TO ROLE DATA ADMIN;' and 'GRANT SELECT ON ALL TABLES IN SCHEMA SALES DATA.TRANSACTIONS TO ROLE REPORTING ROLE;'
- D. Create the database and schema. Then, schedule a task that runs every minute and grants
'SELECT' privileges to 'REPORTING_ROLE on all tables in the schema that don't already have the privilege. - E. Create the database and schema with `OWNER = DATA ADMIN'. Use 'GRANT USAGE ON DATABASE SALES DATA TO ROLE REPORTING ROLE;' and 'GRANT USAGE ON SCHEMA SALES DATA.TRANSACTIONS TO ROLE REPORTING ROLE;'
Answer: A
Explanation:
Future grants are the most efficient and secure way to automatically grant privileges on newly created objects. By using 'GRANT SELECT ON FUTURE TABLES IN SCHEMA SALES_DATA.TRANSACTIONS TO ROLE REPORTING_ROLE;', any new table created in that schema will automatically have SELECT privileges granted to the REPORTING ROLE. Option A only grants usage, not select privileges. Option C and D are inefficient and complex. Option E grants ownership which is not required, and does not address the 'future' requirement.
NEW QUESTION # 55
You have a Snowflake virtual warehouse named 'ANALYTICS WH'. You need to temporarily suspend the warehouse to prevent further credit consumption while a development team debugs a failing query. Which of the following SQL commands is the MOST efficient way to achieve this?
- A.
- B.
- C.
- D.
- E.
Answer: D
Explanation:
The 'ALTER WAREHOUSE ANALYTICS_WH SUSPEND;' command directly and efficiently suspends the warehouse, preventing further credit consumption. Option A is incorrect because warehouse size cannot be set to 'SUSPENDED'. Option C is too drastic as it drops and recreates the warehouse, which is unnecessary. Option D is not a valid Snowflake command. Option E disables auto-suspend, preventing the warehouse from automatically suspending after a period of inactivity, which is the opposite of what is required.
NEW QUESTION # 56
What is the purpose of using the SHOW GRANTS ON SQL command for a specific object?
- A. To list all roles in the account
- B. To list all databases that have access to the object
- C. To list all users in the account
- D. To list all privileges granted on the object
Answer: D
NEW QUESTION # 57
What options are available under the settings for Python worksheets? (Choose any 2 options)
- A. Return Value
- B. Return Type
- C. Handler
- D. Output
Answer: B,C
Explanation:
In Snowflake Python worksheets, settings include theHandlerandReturn Type. The Handler identifies the specific Python function that serves as the entry point for execution-essential when deploying code as stored procedures or user-defined functions. The Return Type defines how Snowflake interprets the output, such as STRING, VARIANT, or TABLE, ensuring proper result rendering. "Return Value" is not a worksheet setting but rather the outcome produced by the code itself, and "Output" is not a configuration option.
NEW QUESTION # 58
What are the key benefits of the Snowflake multi-cluster shared data architecture? (Select TWO).
- A. It allows for independent scaling of compute and storage.
- B. It stores data in a columnar format to improve performance.
- C. It provides enhanced data security features.
- D. It offers near-unlimited concurrency and elasticity.
- E. It optimizes data loading for unstructured data.
Answer: A,D
Explanation:
The Snowflake multi-cluster shared data architecture separates compute and storage, enablingindependent scaling of each. Compute resources (virtual warehouses) operate independently from the centralized storage layer, allowing users to increase compute power without impacting storage costs-or vice versa. This flexibility provides major advantages for cost optimization and workload performance tuning.
The architecture also supportsnear-unlimited concurrency and elasticity. When many users or workloads run simultaneously, Snowflake can automatically add additional clusters to a multi-cluster warehouse, ensuring that no queries experience queuing or performance degradation. This capability is crucial for BI dashboards, ETL pipelines, and large organizations with varied usage patterns.
Columnar storage (option D) is indeed a Snowflake feature but is not unique to the multi-cluster architecture.
Enhanced security arrives via Snowflake's platform-wide mechanisms, not specifically because of multi- cluster architecture. Unstructured data loading (option B) is also not governed by multi-cluster behavior.
NEW QUESTION # 59
You are tasked with using the 'PARSE DOCUMENT' function in Snowflake to extract key information (name, address, phone number) from a large collection of scanned invoices stored as PDF files in an AWS S3 bucket. The invoices have varying formats and quality. Which of the following approaches would be MOST effective to structure the extracted data for analysis?
- A. Directly load PDF files into a relational table's TEXT column and write SQL queries utilizing LIKE and SUBSTRING functions to extract invoice details. Use 'MAX FILE SIZE-16777216' while loading pdf
- B. Use `PARSE DOCUMENT with default settings and load the raw JSON output into a VARIANT column in a Snowflake table. Implement custom SQL queries to extract the desired information from the VARIANT data.
- C. Create a custom UDF (User-Defined Function) that calls 'PARSE_DOCUMENT and then uses regular expressions or other text processing techniques to extract the desired information from the raw text output. Load the extracted data into a relational table.
- D. Employ a combination of 'PARSE DOCUMENT and Snowflake's external functions to integrate with a third-party OCR service for enhanced text extraction. Post-process the results using SQL to load structured data into a table. Use 'MAX FILE SIZE=16777216' while calling parse document
- E. Use `PARSE DOCUMENT with a pre-defined JSON schema to enforce a rigid structure on the extracted data, and load the output directly into a relational table with predefined columns for name, address, and phone number.
Answer: C
Explanation:
Option C provides the most robust and flexible approach. Given the varying formats and quality of the invoices, a pre-defined JSON schema (option B) is unlikely to work effectively. Loading raw JSON into a VARIANT column (option A) requires extensive post-processing. Option D, while potentially effective, introduces the complexity and cost of a third-party OCR service. And MAX_FILE_SIZE parameter controls the maximum size, in bytes, of a single uncompressed file that can be loaded from the stage. Option E is not a scalable and efficient approach.
NEW QUESTION # 60
Which task is performed in the Cloud Services layer in the Snowflake architecture?
- A. Optimizing network traffic
- B. Optimizing SQL queries
- C. Managing user roles
- D. Scaling warehouses up and down
Answer: B
NEW QUESTION # 61
What is the primary use of Snowflake Cortex LLM functions?
- A. Performing traditional SQL joins.
- B. Running machine learning models on structured data.
- C. Optimizing data loading performance.
- D. Analyzing text data for sentiment summarization and other AI/ML tasks.
Answer: D
Explanation:
Cortex LLM functions integrate large language model capabilities directly inside Snowflake. These functions enable:
* Sentiment analysis
* Summarization
* Document question answering
* Text classification
* Embedding generation for vector search
They operate directly on Snowflake data, require no external infrastructure, and support secure in-platform AI workloads.
NEW QUESTION # 62
What does the SELECT * statement do in a Snowflake query?
- A. Retrieves only the primary key column
- B. Retrieves a limited number of rows
- C. Retrieves only distinct values
- D. Retrieves all columns from a specified table or view
Answer: D
Explanation:
The SELECT * statement instructs Snowflake to returnall columnsfrom the referenced table or view. This is commonly used during data exploration, debugging, initial data profiling, and validation steps. It allows users to quickly view the complete dataset structure without manually specifying each column name.
However, while SELECT * retrieves all columns, it does not limit the number of rows. To restrict rows, developers must include aLIMITclause (e.g., SELECT * FROM table LIMIT 10;).
The query does not automatically apply DISTINCT or primary key filtering-Snowflake returns all rows exactly as stored unless additional filtering, WHERE conditions, or ordering are provided.
Though SELECT * is convenient, Snowflake best practices recommend explicitly selecting columns in production workloads to optimize performance and avoid unnecessary scanning of unused fields.
NEW QUESTION # 63
Which of the following statements are TRUE regarding Snowflake's data sharing capabilities and its impact on storage costs and data governance? (Select TWO)
- A. Data sharing in Snowflake involves physically copying the data to the consumer's account, resulting in increased storage costs for both the provider and the consumer.
- B. Data providers can revoke access to shared data at any time, ensuring data governance and security.
- C. Data sharing is only possible between Snowflake accounts within the same cloud region.
- D. Consumers can modify the shared data within their Snowflake account without affecting the original data in the provider's account.
- E. Data sharing in Snowflake allows providers to share live, up-to-date data without physically copying it, resulting in zero storage costs for consumers.
Answer: B,E
Explanation:
Snowflake's data sharing architecture allows sharing data without copying, resulting in zero storage costs for consumers. Providers retain control over their data and can revoke access.
Option A is incorrect as data is not copied. Option D is incorrect because shared data is read-only for consumers. Option E is incorrect as cross-region sharing is possible with replication.
NEW QUESTION # 64
What is the purpose of the SHOW GRANTS SQL command in Snowflake?
- A. To list all databases in the account
- B. To list all roles in the account
- C. To list all roles granted to a user
- D. To list all users in the account
Answer: C
Explanation:
SHOW GRANTS is used to list privileges, including roles assigned to a user.
SHOW GRANTS TO USER <user> reveals all the roles that user inherits.
SHOW ROLES lists roles, SHOW USERS lists users, SHOW DATABASES lists databases-not SHOW GRANTS.
NEW QUESTION # 65
A data engineer needs to load JSON files containing customer reviews stored in an external stage 's3://my-bucket/reviews/'. The JSON structure varies significantly between files, but the goal is to extract 'review_id', 'customer_id', and 'review text' into a Snowflake table 'CUSTOMER REVIEWS'. The engineer is using a COPY INTO statement with a transform. Which of the following is the MOST efficient and correct way to achieve this?
- A. Write a Python UDF to parse the JSON, extract the required fields, and then use the UDF in a COPY INTO statement.
- B. Create separate tables for each JSON structure variation and then use a UNION ALL view to combine the data.
- C. Create a custom file format that defines the expected JSON structure, even though it varies, and rely on Snowflake to handle the variations during the COPY INTO operation.
- D. Use a VARIANT column in 'CUSTOMER_REVIEWS' and load the entire JSON structure. Then, use SQL to extract the required fields during querying.
- E. Use a COPY INTO statement with a JSON path expression to extract the required fields during the load. If a field is missing, handle it with 'NULLIF or similar functions during the COPY INTO.
Answer: E
Explanation:
Option C is the most efficient. Using a JSON path expression in the COPY INTO statement allows extracting the desired fields directly during the load, avoiding the need to load the entire JSON and then process it. 'NULLIF or similar functions can be used to handle cases where a field might be missing in some JSON documents. Options A, B, D, and E are less efficient or impractical. Loading the entire JSON (A) requires post-processing. Creating separate tables (B) is complex and doesn't scale well. Python UDF (D) introduces overhead and performance limitations. Relying on a custom format without explicitly handling variations (E) will lead to errors.
NEW QUESTION # 66
What MOST accurately describes Snowflake?
- A. An ETL tool
- B. An AI data cloud platform
- C. A transactional database
- D. A data warehouse software
Answer: B
NEW QUESTION # 67
A security architect is designing a role hierarchy in Snowflake for a data analytics team. They need to grant specific privileges to different user groups. 'DATA ENGINEER' role should have the ability to create and manage databases. 'DATA ANALYST' role should be able to query data from those databases. 'REPORT USER' role needs read-only access to specific views. The architect wants to ensure minimum privilege and enforce role separation. Which of the following sequences of SQL commands would correctly establish this role hierarchy and grant necessary privileges?
- A. Option A
- B. Option C
- C. Option E
- D. Option D
- E. Option B
Answer: C
Explanation:
Option E is the most complete answer. It creates the roles, grants necessary database creation privileges to DATA_ENGINEER, allows DATA ANALYST to use and query the database, and REPORT USER to use the database and select from the specified view. The role grants establish the hierarchy. Options A and B have incorrect grant statements for select on all tables in the database and lack the creation of Roles at the begining. Option D does not grant the roles to each other, leaving the hierarchy incomplete, and incorrectly attempts to grant select on future tables directly to a view.
NEW QUESTION # 68
Which command is synonymous with the LIST command in Snowflake?
- A. SHOW
- B. LS
- C. LIS
- D. DESC
Answer: B
Explanation:
Snowflake allows users to list files in a stage using the LIST command, and it provides a shorthand alias: LS.
Both commands return identical results, including file name, size, last modified timestamp, and other metadata stored in the stage. This is relevant for internal stages (user, table, named stages) and external stages backed by cloud storage. SHOW and DESC are unrelated commands-SHOW lists Snowflake metadata objects such as tables, schemas, or roles, while DESC describes object structures. "LIS" is not a Snowflake command. Therefore, LS is the exact shorthand synonym for LIST and is used identically in queries.
NEW QUESTION # 69
You are using the Snowsight user interface to monitor the performance of a Snowflake warehouse. You notice that the average query execution time is consistently high. Which of the following actions, performed DIRECTLY through Snowsight's monitoring features, would be MOST effective in identifying the root cause of the performance bottleneck?
- A. Check the Data Marketplace to see if there are any new data sources available.
- B. Increase the warehouse size to the next larger size.
- C. Examine the Query History page, filtering for queries executed by a specific user.
- D. Download the query execution plan for the slowest running query from the last 24 hours.
- E. Analyze the Warehouse Load graph to identify periods of high concurrency and queuing.
Answer: E
Explanation:
Analyzing the Warehouse Load graph is the most effective method using Snowsight's monitoring features. This directly shows periods of high concurrency and queuing, indicating potential resource contention. Query History is useful, but analyzing the load graph is more targeted to warehouse performance as a starting point. Increasing warehouse size without understanding the bottleneck is premature. Downloading query execution plans is useful after identifying slow queries, and the Data Marketplace is irrelevant to performance monitoring.
NEW QUESTION # 70
You are using PARSE DOCUMENT on documents stored in an external stage. You notice that some documents are skipped during processing without any explicit error messages. Which of the following could be the MOST likely reason(s) for this behavior, and how would you address it?
- A. The virtual warehouse is being automatically suspended due to inactivity. Configure auto-resume for the virtual warehouse to ensure it remains active during processing. Also, explicitly specify the virtual warehouse to use by using USE WAREHOUSE
- B. The documents do not contain any text. Verify that the documents actually contain readable text and are not just images or empty files. Remove the files without text
- C. Snowflake encountered an internal error during processing and silently skipped the document.
Review the Snowflake query history for error messages or contact Snowflake support for assistance. - D. The documents are too large. Increase the virtual warehouse size and set the MAX FILE SIZE parameter in the file format to a higher value (e.g., 16MB), and retry the operation.
- E. The documents are in an unsupported format. Ensure that the documents are in a supported format (PDF, DOCX, etc.) and that Snowflake has the necessary permissions to access the external stage. Check the 'VALIDATE function to verify if the data file is valid before parsing.
Answer: A,E
Explanation:
Option B is a common cause, where unsupported format and permissions are key things to consider and `VALIDATE' helps ensure data is valid. Option C is also correct, because virtual warehouse suspension can interrupt processing and specifying the warehouse ensures it's used.
Option A, while relevant to performance, doesn't explain silent skipping unless the files grossly exceed limits. Option D is possible but less likely than B or C and harder to diagnose without specific error messages. Option E is less likely, because even if the content is not parseable, it still doesn't explain why document is skipped silently.
NEW QUESTION # 71
What is a key capability of the Snowflake virtual warehouse?
- A. It can store data permanently.
- B. It can be dynamically scaled up or down.
- C. It can be located on-premises.
- D. It supports unlimited concurrency.
Answer: B
Explanation:
A virtual warehouse provides compute resources in Snowflake and can be resized (scaled up or down) at any time. Scaling up increases compute power for intensive workloads, while scaling down reduces cost for lighter workloads.
Virtual warehouses do not store data-that is handled by Snowflake's independent storage layer. They are not on-premises and do not provide unlimited concurrency; Snowflake supports high concurrency using multi- cluster warehouses but not boundless concurrency.
NEW QUESTION # 72
How can roles access objects in Snowflake? (Choose any 2 options)
- A. By configuring network settings
- B. By storing raw data
- C. By being directly assigned privileges on the objects
- D. By inheriting privileges from parent roles
Answer: C,D
Explanation:
Roles gain access to objects in Snowflake throughdirect privilege grantsor throughinheritancewithin a role hierarchy. A role may be explicitly granted privileges such as SELECT, OWNERSHIP, or USAGE.
Alternatively, if a role is assigned to another role, it inherits all privileges of its parent role. Neither network settings nor data storage processes determine role access.
NEW QUESTION # 73
......
New (2026) Snowflake SOL-C01 Exam Dumps: https://www.prepawayete.com/Snowflake/SOL-C01-practice-exam-dumps.html
SOL-C01 Practice Dumps - Verified By PrepAwayETE Updated 218 Questions: https://drive.google.com/open?id=1ixipc3lE3ctZ3_s-yIMTCuply0UUNbrE