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Snowflake SOL-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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.
Topic 4	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.

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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q33-Q38):

NEW QUESTION # 33
A data warehouse contains a table 'SALES TRANSACTIONS that is frequently updated. You need to ensure that the data is protected against accidental deletions and updates, and you need to be able to restore the data to a specific point in time. Which of the following Snowflake features and configurations should you implement to achieve this goal effectively?

A. Rely solely on Fail-safe as this guarantees long-term data recovery and provides automated recovery capabilities. Set Data Retention parameter to 1.
B. Set the `DATA RETENTION TIME IN DAYS' parameter to a sufficiently long period (e.g., 90 days) and regularly create backups using Snowflake's data export feature.
C. Set the `DATA RETENTION TIME IN DAYS' parameter at the account level to a sufficient value, and understand that Fail-safe provides an additional layer of protection, although not directly accessible.
D. Enable replication across multiple regions to protect against regional outages and ensure data availability. This is the only protection required.

Answer: C

Explanation:
Setting the parameter enables Time Travel, which allows restoring data to a specific point in the past within the retention period. Fail-safe provides an additional layer of protection beyond Time Travel, but it's not directly accessible for user- initiated recovery. Replication (Option A) is for disaster recovery but doesn't help with user errors. Backups (Option B) are useful but add complexity compared to Time Travel. Option D relies solely on Fail-safe, which is not user- accessible. Note, it is best to only provide time travel on required databases to save money on storage costs, account level might be unnecessary. Therefore, C is still better option than setting at table level also.

NEW QUESTION # 34
A data engineer is tasked with ensuring that only authorized personnel can access sensitive data within a Snowflake database named 'SALES DB'. They need to implement a row-level security policy that filters rows based on the current user's role. A table named 'CUSTOMER DATA exists with a column named 'REGION'. Which of the following steps is the MOST effective and secure way to achieve this, considering minimal administrative overhead and leveraging Snowflake's built-in features?

A. Create a stored procedure that dynamically generates SQL queries based on the current user's role and executes them against 'CUSTOMER DATA'.
B. Implement a custom application logic layer that retrieves all data from 'CUSTOMER_DATA' and filters it based on the user's role before displaying it.
C. Create a masking policy on the 'REGION' column that replaces values with NULL based on the current user's role.
D. Create a view that joins `CUSTOMER DATA' with `INFORMATION SCHEMA.APPLICABLE ROLES and applies a filter based on the current user's role and the REGION' column.
E. Create a row access policy on the 'CUSTOMER DATA' table that filters rows based on the current user's role and the 'REGION' column.

Answer: E

Explanation:
Row access policies provide the most secure and manageable way to implement row-level security in Snowflake. They are tightly integrated with the database and enforce access control at the data level, reducing the risk of data leakage and simplifying administration. Options A, B, C and E are either less efficient, less secure, or more complex to manage.

NEW QUESTION # 35
You have a Python script that uses Snowpark to connect to Snowflake. You want to ensure that the connection parameters, such as account identifier, username, password, and database, are securely managed and not hardcoded directly in the script. Which of the following strategies would be most effective and secure for managing these sensitive connection parameters?
Choose all that apply:

A. Use Snowflake's Secret object to store credentials, and use Snowpark to retrieve these secrets for connection.
B. Utilize environment variables to store the connection parameters and access them from the Python script.
C. Store the connection parameters in a configuration file encrypted with a strong encryption algorithm, and decrypt it within the Python script using a key stored separately and securely.
D. Hardcode the parameters within the Python script, but obfuscate them using Base64 encoding.
E. Store the connection parameters in a plain text file in a secure location on the server.

Answer: A,B,C

Explanation:
Environment variables (Option B) are a common and relatively secure way to store sensitive information, as they are not directly visible in the code. Snowflake secrets (Option C) offer managed secrets, which is a dedicated, secure method to store and retrieve credentials.
Encrypting the configuration file (Option D) adds an extra layer of security, especially if the decryption key is managed separately. Storing credentials in a plain text file (Option A) is highly insecure. Obfuscation (Option E) is not a form of strong security, as Base64 encoding is easily reversible.

NEW QUESTION # 36
Within a Snowflake Notebook, you have a Python script that performs several data transformations using Pandas DataFrames and then attempts to load the transformed data into a new Snowflake table. The script fails intermittently with 'MemoryError'. Which of the following strategies could you employ to mitigate the 'MemoryError' when working with Snowflake Notebooks and Python? (Choose all that apply.)

A. Utilize the `COPY INTO' command directly from Snowflake to load data into the table, bypassing Pandas DataFrames entirely.
B. Increase the virtual warehouse size allocated to the Snowflake session.
C. Reduce the number of concurrent Snowflake Notebook sessions running.
D. Optimize the Pandas DataFrame's data types to use less memory (e.g., using 'int32 instead of
'int64' where appropriate).
E. Implement chunking: Read the data from Snowflake in smaller batches, process each batch individually, and append the results to the target table.

Answer: A,D,E

Explanation:
Options B, C, and D are correct. A MemoryError indicates that the Python process within the Snowflake Notebook is running out of memory. Chunking (B) allows processing of data in smaller, manageable pieces. Optimizing Pandas DataFrame data types (C) reduces the memory footprint of each DataFrame. Using 'COPY INTO' (D) leverages Snowflake's internal data loading capabilities, avoiding the need to load the entire dataset into a Pandas DataFrame. Increasing the virtual warehouse size (A) primarily affects query performance within Snowflake, not the memory available to the Python process in the Notebook. Reducing the number of concurrent sessions might help if resource contention is the root cause, but is less directly related to the error, so E is not the strongest solution.

NEW QUESTION # 37
What is the purpose of Time Travel?

A. To ensure that users' data can be recovered at any time
B. To facilitate the loading of historical data into Snowflake
C. To automatically manage timestamp data types
D. To allow users to access historical data

Answer: D

Explanation:
Time Travel enables Snowflake users to query, clone, or restore historical versions of data. This includes retrieving previous states of tables, schemas, or databases-even after updates, deletes, or drops. Time Travel operates within a retention period (default: 1 day, up to 90 days on higher editions).
Users can query historical data using the AS OF or BEFORE clause, restore dropped objects, and clone databases at specific points in time for backup or analysis.
Time Travel doesnotautomatically manage timestamp data types. It does not guarantee indefinite recovery- after the retention window expires, data moves into Fail-safe. It also is not primarily designed for loading historical datasets; its purpose is to access past states of Snowflake-managed data.
Thus, the correct purpose is to enable access to historical data inside Snowflake.

NEW QUESTION # 38
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