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Snowflake ARA-C01 Exam Syllabus Topics:

Section	Objectives
Data Pipelines and Data Engineering	- Data ingestion and transformation 1. Batch and streaming ingestion 2. Snowpipe and continuous data loading - Data orchestration 1. Pipeline design patterns 2. Tasks and Streams usage
Performance and Optimization	- Cost optimization 1. Resource monitoring and cost control 2. Storage and compute efficiency - Query performance tuning 1. Query profiling and optimization techniques 2. Warehouse sizing and scaling
Data Architecture and Design	- Data modeling and schema design 1. Semi-structured data modeling 2. Star and snowflake schema concepts - Design scalable Snowflake architectures 1. Account and environment strategy 2. Multi-cluster virtual warehouse design
Security, Governance, and Compliance	- Data governance 1. Data classification and masking policies 2. Audit and compliance features - Access control and security model 1. Role-based access control (RBAC) 2. Network security and authentication
Data Sharing and Collaboration	- Secure data sharing 1. Data marketplace concepts 2. Cross-account data sharing

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q97-Q102):

NEW QUESTION # 97
The Data Engineering team at a large manufacturing company needs to engineer data coming from many sources to support a wide variety of use cases and data consumer requirements which include:
1) Finance and Vendor Management team members who require reporting and visualization
2) Data Science team members who require access to raw data for ML model development
3) Sales team members who require engineered and protected data for data monetization What Snowflake data modeling approaches will meet these requirements? (Choose two.)

A. Create a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.
B. Consolidate data in the company's data lake and use EXTERNAL TABLES.
C. Create a raw database for landing and persisting raw data entering the data pipelines.
D. Create a single star schema in a single database to support all consumers' requirements.
E. Create a set of profile-specific databases that aligns data with usage patterns.

Answer: C,E

Explanation:
To accommodate the diverse needs of different teams and use cases within a company, a flexible and multi- faceted approach to data modeling is required.
Option B:By creating a raw database for landing and persisting raw data, you ensure that the Data Science team has access to unprocessed data for machine learning model development. This aligns with the best practices of having a staging area or raw data zone in a modern data architecture where raw data is ingested before being transformed or processed for different use cases.
Option C:Having profile-specific databases means creating targeted databases that are designed to meet the specific requirements of each user profile or team within the company. For the Finance and Vendor Management teams, the data can be structured and optimized for reporting and visualization. For the Sales team, the database can include engineered and protected data that is suitable for data monetization efforts.
This strategy not only aligns data with usage patterns but also helps in managing data access and security policies effectively.

NEW QUESTION # 98
When loading data into a table that captures the load time in a column with a default value of either CURRENT_TIME () or CURRENT_TIMESTAMP() what will occur?

A. All rows loaded using a specific COPY statement will have varying timestamps based on when the rows were inserted.
B. All rows loaded using a specific COPY statement will have the same timestamp value.
C. Any rows loaded using a specific COPY statement will have varying timestamps based on when the rows were created in the source.
D. Any rows loaded using a specific COPY statement will have varying timestamps based on when the rows were read from the source.

Answer: B

Explanation:
According to the Snowflake documentation, when loading data into a table that captures the load time in a column with a default value of either CURRENT_TIME () or CURRENT_TIMESTAMP(), the default value is evaluated once per COPY statement, not once per row. Therefore, all rows loaded using a specific COPY statement will have the same timestamp value. This behavior ensures that the timestamp value reflects the time when the data was loaded into the table, not when the data was read from the source or created in the source. Reference:
Snowflake Documentation: Loading Data into Tables with Default Values
Snowflake Documentation: COPY INTO table

NEW QUESTION # 99
The IT Security team has identified that there is an ongoing credential stuffing attack on many of their organization's system.
What is the BEST way to find recent and ongoing login attempts to Snowflake?

A. Query the LOGIN_HISTORY view in the ACCOUNT_USAGE schema in the SNOWFLAKE database.
B. View the Users section in the Account tab in the Snowflake UI and review the last login column.
C. View the History tab in the Snowflake UI and set up a filter for SQL text that contains the text "LOGIN".
D. Call the LOGIN_HISTORY Information Schema table function.

Answer: D

NEW QUESTION # 100
A company stores customer data in Snowflake and must protect Personally Identifiable Information (PII) to meet strict regulatory requirements.
What should an Architect do?

A. Use tag-based masking policies for columns containing PII.
B. Use row-level security to mask PII data.
C. Separate PII into different tables and grant access as needed.
D. Create secure views for PII data and grant access as needed.

Answer: A

Explanation:
Tag-based masking policies provide a scalable and centralized way to protect PII across many tables and schemas (Answer B). By tagging columns that contain PII and associating masking policies with those tags, Snowflake automatically enforces masking rules wherever the tagged columns appear. This approach reduces administrative overhead and ensures consistent enforcement as schemas evolve.
Row access policies control row visibility, not column masking. Secure views and table separation can protect data but introduce significant maintenance complexity and do not scale well across large environments.
Snowflake best practices-and the SnowPro Architect exam-emphasize tag-based governance for sensitive data.
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NEW QUESTION # 101
A user is executing the following command sequentially within a timeframe of 10 minutes from start to finish:

What would be the output of this query?

A. Table T_SALES_CLONE successfully created.
B. The offset -> is not a valid clause in the clone operation.
C. Time Travel data is not available for table T_SALES.
D. Syntax error line 1 at position 58 unexpected 'at'.

Answer: A

Explanation:
The query is executing a clone operation on an existing table t_sales with an offset to account for the retention time. The syntax used is correct for cloning a table in Snowflake, and the use of the at(offset => -60*30) clause is valid. This specifies that the clone should be based on the state of the table 30 minutes prior (60 seconds * 30). Assuming the table t_sales exists and has been modified within the last 30 minutes, and considering the data_retention_time_in_days is set to 1 day (which enables time travel queries for the past 24 hours), the table t_sales_clone would be successfully created based on the state of t_sales 30 minutes before the clone command was issued.

NEW QUESTION # 102
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