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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Security, Governance, and Data Sharing	- Access control and security 1. Role-based access control (RBAC) 2. Authentication and encryption concepts - Data sharing and governance 1. Data masking and policies 2. Secure data sharing
Data Transformation and Analysis	- SQL-based transformations 1. Joins, aggregations, window functions 2. Semi-structured data (VARIANT, JSON, XML) - Analytical workloads 1. Materialized views and caching 2. Query optimization for analytics
Data Modeling and Performance Optimization	- Performance tuning 1. Clustering and pruning techniques 2. Warehouse sizing and auto-suspend/auto-resume - Modeling approaches in Snowflake 1. Data normalization vs denormalization 2. Star and snowflake schemas
Snowflake Architecture and Data Platform Fundamentals	- Snowflake architecture concepts 1. Cloud services layer, compute layer, storage layer 2. Virtual warehouses and scaling - Data platform fundamentals 1. Separation of storage and compute 2. Data lifecycle in Snowflake
Data Loading and Unloading	- Data ingestion methods 1. COPY INTO and bulk loading 2. Continuous ingestion and Snowpipe concepts - Data export 1. UNLOAD and external stages

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q24-Q29):

NEW QUESTION # 24
Your organization is migrating its data warehouse to Snowflake. You need to monitor the resource consumption of different users. You want to identify which users are running the most expensive queries (in terms of credits consumed) over the last 7 days. You need to create a query using system functions to achieve this. Which of the following queries will accurately provide this information?

A. SELECT user_name, SUM(credits_used_cloud_services) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time >=DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;
B. SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.warehouse_metering_history WHERE start_time >= DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;
C. SELECT user_name, SUM(credits_used_compute) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time DATEADD(day, -7, CURRENT TIMESTAMP()) GROUP BY user_name ORDER BY DESC;
D. SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.query_history WHERE start_time DATEADD(day, -7, GROUP BY user_name ORDER BY DESC;
E. SELECT user_name, SUM(credits_used) AS total_credits_used FROM snowflake.account_usage.execution_history WHERE start_time >= DATEADD(day, -7, GROUP BY user_name ORDER BY DESC;

Answer: C

Explanation:
The correct query should use the 'snowflake.account_usage.query_history' view and specifically sum the column as this reflects the credits used for the compute resources by query. Option C accurately reflects this. Option A is incorrect because the 'credits_used' column in 'query_history' does not give a direct credit consumption cost based on computation. Option B uses 'execution_history' which does not aggregate the same detailed credit usage information. Option D sums credits used for cloud services not the compute. Option E references warehouse metering history, not specific users' query execution history.

NEW QUESTION # 25
You are troubleshooting a slow-running query that performs a complex transformation on a large dataset using a series of CTEs. You've identified one particular CTE, , as contributing significantly to the overall execution time. Examination of the query profile reveals high spillover to local storage during the execution of this CTE. Which of the following actions could potentially reduce spillover and improve the performance of the CTE? Select all that apply.

A. Create a materialized view from the 'calculate metrics CTE.
B. Partition the underlying tables used in the CTE.
C. Rewrite the CTE to use smaller intermediate tables.
D. Increase the warehouse size.
E. Reduce the 'MAX MEMORY USAGE IN parameter at the session level.

Answer: C,D

Explanation:
Increasing the warehouse size (A) provides more memory, which directly reduces spillover. Rewriting the CTE to use smaller intermediate tables (C) reduces the memory footprint of the operation. Partitioning the tables (B) is not directly supported in Snowflake. Creating a materialized view from the CTE (D) can improve performance if the CTE's results are frequently reused, but it does not necessarily address the spillover issue directly. Reducing the 'MAX (E) would worsen spillover. Using temporary tables (E) will not necessarily impact spilling if the memory limits are still exceeded within the query runtime.

NEW QUESTION # 26
What factors should be considered when evaluating which transformations are required in data discovery? (Select all that apply)

A. Data consistency
B. Data redundancy
C. Data normalization
D. Business use cases

Answer: A,C,D

Explanation:
Evaluating necessary transformations involves considering data consistency, normalization, and alignment with business use cases.

NEW QUESTION # 27
Which aspects are important when making predictions based on data for forecasting purposes?
(Select all that apply)

A. Incorporating statistical methods for accurate predictions
B. Relying solely on historical data without considering external factors
C. Using only basic arithmetic functions for forecasting
D. Considering trends and anomalies in historical data

Answer: A,D

Explanation:
Incorporating statistical methods and considering trends and anomalies are crucial for accurate predictions in forecasting.

NEW QUESTION # 28
What functionalities are available when a Snowflake worksheet is shared with other users? (Select TWO).

A. Users with edit permissions can view past versions of the worksheet.
B. Collaborators can share the worksheet across Snowflake accounts.
C. If the worksheet is being edited, the collaborators will be able to see these edits in real-time.
D. If multiple users edit and run a shared worksheet at the same time, each run of the worksheet will create a new version.
E. Whenever a user with permissions runs a worksheet, the existing version history of the worksheet will be overwritten.

Answer: C,D

Explanation:
Snowsight, the modern web interface for Snowflake, introduces robust collaboration features through shared worksheets. Understanding the synchronization and versioning logic is critical for the Data Presentation and Data Visualization domain.
When a worksheet is shared with collaborators, Snowflake enables real-time collaboration (Option D). This means that as one user types or modifies SQL code, those changes are immediately visible to all other users who have the worksheet open. This functionality is similar to co-authoring in modern document editors, allowing data teams to debug or develop complex queries synchronously without the need for manual copy- pasting or constant screen sharing.
Furthermore, Snowflake manages the execution history of shared worksheets through a structured versioning system (Option C). If multiple users are interacting with the same worksheet, Snowflake does not overwrite a single "master" state during execution. Instead, every time a user executes the worksheet (or a portion of it), Snowflake creates a new version of the worksheet content associated with that specific run. This ensures that the code used to generate a particular result set is preserved and tied to that specific Query ID in the history.
Evaluating the Options:
* Option A is incorrect because while users can see current edits, viewing a full, navigable history of
"past versions" is not a standard feature in the same way it is in dedicated version control systems like Git.
* Option B is incorrect as worksheet sharing is restricted to users within the same Snowflake account.
Sharing across accounts requires the use of Data Shares or Private Listings.
* Option E is incorrect because Snowflake preserves the history rather than overwriting it, allowing analysts to audit who ran what code and when.
* Options C and D are the 100% correct answers. They define the synchronous and persistent nature of Snowsight collaboration, ensuring that teams can work together effectively while maintaining an immutable trail of executions.

NEW QUESTION # 29
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