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Snowflake SOL-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	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.

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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q71-Q76):

NEW QUESTION # 71
A Snowflake table 'CUSTOMER DATA' contains Personally Identifiable Information (PII). You need to mask the 'EMAIL' column for users with the 'ANALYST' role, while allowing users with the
'DATA SCIENTIST role to view the unmasked data. Assuming both roles already exist, which of the following Snowflake features, in combination, would BEST accomplish this?

A. Data Encryption and Network Policies.
B. External Tables and Secure Views.
C. Transient Tables and User-Defined Functions.
D. Dynamic Data Masking and Column-Level Security.
E. Dynamic Data Masking and Row-Level Security.

Answer: D

Explanation:
Dynamic Data Masking is the primary feature for masking sensitive data. Combining it with Column- Level Security (granted via roles) allows you to selectively mask the ` EMAIL' column based on the user's role. Row-Level Security filters rows, not columns. External Tables, Secure Views, Data Encryption, Network Policies, Transient Tables, and UDFs are not directly relevant to this specific masking requirement.

NEW QUESTION # 72
A data scientist is working on a machine learning project using Snowflake Notebooks. They have a dataset stored in Snowflake and need to perform feature engineering. They want to write a Python function that takes a Snowflake table name and a list of SQL expressions as input, executes these expressions against the table, and returns a Pandas DataFrame containing the new features. Which approach is MOST suitable for creating and executing this function within a Snowflake Notebook, minimizing data transfer outside of Snowflake?

A. Use the `snowflake.connectors library to connect to Snowflake, execute each SQL expression using the method, and append the results to a Pandas DataFrame.
B. Utilize the `sqlalchemy' library to establish a connection to Snowflake, construct the SQL query dynamically using SQLAlchemy's expression language, and fetch the results into a Pandas DataFrame.
C. Use the Snowflake web UI to create a view containing the feature engineered data. Load that view into the notebook as a Pandas DataFrame.
D. Use the `snowflake.snowpark.functions.call_udf to call a UDF from Snowflake notebooks and create a dataframe.
E. Create a Snowflake User-Defined Function (UDF) that encapsulates the feature engineering logic, and then call this UDF from the notebook using Snowpark or standard SQL.

Answer: E

Explanation:
Creating a UDF is the most efficient way to perform feature engineering because the computation happens within Snowflake's compute engine. Snowpark's or standard SQL can then be used to call and retrieve data from UDFs. This minimizes data transfer. Option A still fetches intermediate data. Options D create extra steps and extra objects. Option E isn't the standard approach when working with Snowpark in Notebooks.

NEW QUESTION # 73
A data engineer is tasked with designing a Snowflake solution for a financial services company that needs to perform real-time fraud detection. The solution needs to leverage external models trained in Python using Snowpark ML and ingest data from various sources, including Kafka streams and S3 buckets. Which of the following architectural choices would best leverage the Snowflake A1 Data Cloud capabilities for this scenario?

A. Employ Snowflake's Snowpipe for Kafka integration, use Snowflake Tasks to schedule data loading from S3, and leverage Snowflake's native SQL capabilities for fraud detection without employing external models.
B. Utilize Snowflake's Snowpark ML to deploy and manage models directly within Snowflake, integrate with Kafka using Snowflake Kafka connector, and use external tables for S3 data access.
C. Create a separate Spark cluster to process Kafka streams and use the Snowflake Spark connector to write the processed data to Snowflake. Use COPY INTO for S3 data, and deploy the ML model using Snowflake external functions calling a Databricks cluster.
D. Use Snowflake external functions to call a separate SageMaker endpoint for model inference and load data from Kafka using Snowpipe and S3 using COPY INTO.
E. Use Snowflake's Streamlit integration for real-time data visualization of Kafka streams, and use Snowpark ML to deploy and manage models directly within Snowflake, loading data from S3 via COPY INTO.

Answer: B,E

Explanation:
Option B and E offer the best solutions. Option B leverages Snowflake's Snowpark ML for model management within Snowflake, Snowflake Kafka connector for direct Kafka integration, and external tables to avoid data duplication for S3. Option E complements B by adding Streamlit integration for Real Time Visualization of Kafka Streams. Option A, C and D introduce unnecessary external dependencies (SageMaker, Spark, Databricks) and don't fully utilize Snowflake's native capabilities for data processing and ML model deployment. Choosing native capabilities minimizes complexity and operational overhead.

NEW QUESTION # 74
What is the main advantage of using Materialized Views in Snowflake?

A. They always contain the latest data in real-time.
B. They can be directly updated by DML statements.
C. They are primarily used for data masking.
D. They physically store pre-computed results to improve query performance.

Answer: D

Explanation:
Snowflake Materialized Views physically store the results of a defined query. This pre-computation significantly improves performance for repetitive or complex queries, especially those based on aggregations and joins. Snowflake automatically maintains materialized views asynchronously. They do not update in real- time, cannot be changed through DML, and are not intended for masking-dynamic masking policies serve that purpose.

NEW QUESTION # 75
What is the purpose of the SHOW GRANTS SQL command in Snowflake?

A. To list all users in the account
B. To list all databases in the account
C. To list all roles in the account
D. To list all roles granted to a user

Answer: D

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 # 76
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