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Snowflake SOL-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Data Loading and Virtual Warehouses: This domain covers loading structured, semi-structured, and unstructured data using stages and various methods, virtual warehouse configurations and scaling strategies, and Snowflake Cortex LLM functions for AI-powered operations.
Topic 2	Identity and Data Access Management: This domain focuses on Role-Based Access Control (RBAC) including role hierarchies and privileges, along with basic database administration tasks like creating objects, transferring ownership, and executing fundamental SQL commands.
Topic 3	Data Protection and Data Sharing: This domain addresses continuous data protection through Time Travel and cloning, plus data collaboration capabilities via Snowflake Marketplace and private Data Exchange sharing.
Topic 4	Interacting with Snowflake and the Architecture: This domain covers Snowflake's elastic architecture, key user interfaces like Snowsight and Notebooks, and the object hierarchy including databases, schemas, tables, and views with practical navigation and code execution skills.

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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q102-Q107):

NEW QUESTION # 102
A data engineer is tasked with loading JSON files containing customer reviews from an external stage into a Snowflake table. The JSON files have varying schemas and nested structures.
Which of the following methods is the MOST efficient and scalable way to ingest and query this data, minimizing the need for upfront schema definition?

A. Create a relational table with predefined columns based on the expected schema. Use a COPY INTO statement to load the data, handling schema variations through error handling in the COPY INTO statement.
B. Use a stored procedure to parse the JSON files and insert the data into a relational table with a predefined schema. Implement error handling within the stored procedure to handle schema variations.
C. Load the JSON data into a cloud storage location, then use a Spark cluster to process and transform the data into a structured format before loading it into Snowflake.
D. Create an external table using the CREATE EXTERNAL TABLE command, pointing to the external stage.
Define a schema on the external table based on a representative JSON file. Query the data directly from the external table.
E. Create a VARIANT column in a Snowflake table. Load the JSON data into the VARIANT column using a COPY INTO statement. Query the data using dot notation and FLATTEN function as needed.

Answer: E

Explanation:
Using a VARIANT column is the most efficient way to load JSON data with varying schemas because it allows you to load the data as is without defining a rigid schema upfront. Dot notation and the FLATTEN function allow you to query the data flexibly. Creating a relational table (Option A) requires defining a schema upfront which is not ideal for varying schemas. External tables (Option C) still require a schema definition. Stored procedures (Option D) can be complex and less scalable. Using Spark (Option E) adds unnecessary complexity and cost for this scenario.

NEW QUESTION # 103
You have enabled directory tables on an external stage pointing to an AWS S3 bucket. The S3 bucket contains millions of small JSON files. When you query the directory table, you notice that the query performance is slow, even when filtering by 'METADATA$FILENAME'. What strategies can you implement to optimize the performance of querying the directory table?

A. Increase the virtual warehouse size used for querying the directory table.
B. Reduce the number of files in the S3 bucket. Directory tables perform better with fewer files.
C. Create a materialized view on top of the directory table, including the 'METADATA$FILENAME , , and any other frequently filtered columns.
D. Create an index on the 'METADATA$FILENAME column of the directory table.
E. Periodically refresh the directory table using 'ALTER STAGE REFRESH;'.

Answer: C

Explanation:
Creating a materialized view (Option B) is the most effective way to optimize the performance of querying a directory table. Materialized views pre-compute and store the results of a query, allowing for faster retrieval of data, especially when the underlying table (the directory table in this case) is large. Increasing warehouse size (Option A) can help to a certain extent, but a materialized view provides a more significant performance boost. Refreshing the directory table (Option C) keeps the metadata up- to-date but doesn't directly improve query performance.
Creating an index (Option D) is not possible on directory tables. While reducing the number of files (Option E) might help slightly, it's often impractical and doesn't address the underlying issue of querying a large dataset. It's best to handle millions of files with proper indexing and data partitioning.

NEW QUESTION # 104
You have a table named 'SALES DATA' in your Snowflake account. You want to create a clone of this table for testing purposes, but you also want to mask certain columns containing sensitive customer information (e.g., email addresses, phone numbers) in the cloned table. How can you achieve this with minimal impact on performance and storage?

A. Create a view on top of 'SALES DATA' with masking logic for the sensitive columns, and then create a clone of the view as 'TEST SALES DATA'.
B. Create a clone of 'SALES_DATX using 'CREATE TABLE CLONE SALES_DATA;' then create a task to periodically apply masking to the sensitive data.
C. Create a clone of 'SALES DATA' using 'CREATE TABLE TEST SALES DATA CLONE SALES DATA;' and then manually update the sensitive columns with dummy data.
D. Export the data from 'SALES DATA' to cloud storage, apply masking to the sensitive columns during the export process, and then import the masked data into a new table 'TEST SALES DATA'.
E. Create a clone of 'SALES DATA' using 'CREATE TABLE TEST SALES DATA CLONE SALES DATA;' and then create masking policies on the sensitive columns in ` TEST SALES DATA'.

Answer: E

Explanation:
The most efficient way is to clone the table and then apply masking policies on the cloned table.
Masking policies ensure that data is masked dynamically without altering the underlying data in the original table. This approach minimizes storage usage (due to zero-copy cloning) and performance impact (since masking policies are optimized by Snowflake). Option A is manual and not scalable. Option C involves data export/import which is slower and consumes more resources. Cloning the view doesn't clone underlying data, so it's not suitable for testing changes on data. Option E requires ongoing processing to apply masking periodically.

NEW QUESTION # 105
Which of the following are types of tables in Snowflake? (Choose any 3 options)

A. Permanent
B. Transient
C. Virtual
D. Temporary

Answer: A,B,D

Explanation:
Snowflake provides three main table types:Permanent,Transient, andTemporary. Permanent tables support full Time Travel and Fail-safe, and are designed for long-term storage. Transient tables do not include Fail- safe and are best suited for intermediate or less durable datasets. Temporary tables exist only for the duration of the user session that created them, making them ideal for staging or ephemeral workloads. Snowflake does not have a table type called "Virtual"; therefore, the correct options are Permanent, Temporary, and Transient.

NEW QUESTION # 106
You are using Snowsight to monitor a data pipeline that involves several tasks chained together using 'AFTER' dependencies. One of the intermediate tasks, is failing intermittently. Which of the following Snowsight features would be MOST helpful in quickly identifying the root cause of the failures and assessing the impact on downstream tasks?

A. The Resource Monitor page to see if warehouses are being over utilized and impacting all workloads.
B. The task history view in Snowsight, focusing on the execution details, error messages, and query profiles for and its predecessor tasks.
C. The role hierarchy page to see if the role executing the task has the correct privileges.
D. The Snowsight worksheet interface, by running SELECT statements on the underlying data in the tables involved.
E. The data lineage feature in Snowsight to visualize the data flow and dependencies between the tables used by the pipeline.

Answer: B

Explanation:
The task history view in Snowsight provides specific information about the execution of each task, including error messages, query profiles, and execution times. This allows you to pinpoint the cause of the failure in and examine the history of predecessor tasks to identify any upstream issues. The Task details page shows which tasks were executed after the specific task. While data lineage (Option C) is helpful for understanding data flow, it doesn't directly show execution details. Option A (worksheets) is a general debugging tool but not the most efficient for task- specific issues. Options D and E might be relevant but are less direct than examining the task history.

NEW QUESTION # 107
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2026 100% Free SOL-C01–Accurate 100% Free Exam Pass Guide | Snowflake Certified SnowPro Associate - Platform Certification Valid Test Testking

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