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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q26-Q31):

NEW QUESTION # 26
You are tasked with creating a dashboard to visualize website traffic data'. The data is stored in a Snowflake table 'WEB EVENTS' with the following columns: 'EVENT_ID', 'USER_ID', 'EVENT_TIMESTAMP', 'PAGE_URL', 'SESSION_ID', and 'DEVICE_TYPE. You need to calculate the average session duration for each device type, but the 'WEB EVENTS' table only contains individual events, not session start and end times. Assume a session is defined by a unique 'SESSION ID' for each 'USER ID. You are given a window function to calculate the start time and end time for a given 'SESSION ID': First_value and last_value. What steps are crucial to calculate average session duration by 'DEVICE TYPE?

A. Simply group by 'DEVICE_TYPE and calculate the average ' EVENT_TIMESTAMP'
B. Calculate the sum of all 'EVENT TIMESTAMP' values for each 'USER ID. Group by 'DEVICE_TYPE and calculate the average.
C. Use window functions to determine the start and end 'EVENT TIMESTAMP for each 'SESSION ID. Calculate the difference between these start and end times. Then, group by 'DEVICE _ TYPE and calculate the average of these differences.
D. Calculate the difference between the maximum and minimum 'EVENT _ TIMESTAMP' for each ' USER_ID. Then, group by 'DEVICE _ TYPE and calculate the average of these differences.
E. Calculate median of event timestamp using percentile_cont. The calculate difference between median and max timestamps.

Answer: C

Explanation:
Option B correctly identifies the steps to calculate session duration. First determine Session Start and End time. 1) Use window functions to identify the start and end timestamp for a given SESSION ID. 2) Calculate the difference between these timestamps. 3) Group by device type to arrive at the averages for session duration across the device_type. Option A is incorrect as it does not represent the session duration which needs to be calculated for a given Session_ID.

NEW QUESTION # 27
Why are Stored Procedures valuable in data analysis using SQL?

A. They are exclusively used for one-time data operations.
B. Stored Procedures enable custom and repeated data operations, enhancing efficiency.
C. Stored Procedures solely facilitate data visualization.
D. They restrict the execution of repetitive tasks, limiting efficiency.

Answer: B

Explanation:
Stored Procedures aid in data analysis by enabling custom and repeated data operations, enhancing efficiency.

NEW QUESTION # 28
Which approach is most suitable for making data-driven predictions?

A. Basic SQL queries
B. Using built-in SQL functions for statistical analysis
C. Ignoring historical data trends
D. Relying on external data sources only

Answer: B

NEW QUESTION # 29
A marketing team wants to visualize customer engagement across different channels (email, website, social media). They have engagement data stored in a Snowflake table 'CUSTOMER ENGAGEMENT' with columns 'CUSTOMER ID, 'CHANNEL', 'EVENT TYPE', 'EVENT TIMESTAMP', and 'EVENT VALUE' (representing engagement score). They need a dashboard that efficiently displays the total engagement score per channel for the last 7 days, allowing for quick drill-down into individual customer behavior. Which approach provides the MOST optimized query and view structure for this requirement, prioritizing performance for a large dataset?

A. Create a view that directly aggregates data from 'CUSTOMER_ENGAGEMENT for the last 7 days in the dashboard query itself. Use 'GROUP BY CHANNEL' and ' SUM(EVENT_BALUE)'.
B. Create a materialized view 'MV CUSTOMER ENGAGEMENT that pre-aggregates the total engagement score per channel for the last 7 days, refreshed daily using a Snowflake task. The dashboard query then selects directly from this materialized view.
C. Use a stored procedure to calculate the engagement score and update a temporary table daily. The dashboard queries this temporary table.
D. Create a standard view that aggregates data from 'CUSTOMER ENGAGEMENT using 'GROUP BY CHANNEL' , but without filtering for the last 7 days. Filter for the last 7 days within the dashboard query.
E. Load the entire 'CUSTOMER ENGAGEMENT table into the dashboard tool's internal memory and perform the aggregation and filtering there.

Answer: B

Explanation:
Materialized views (B) are the most performant option because they pre-compute and store the aggregated data. A daily refresh using a Snowflake task ensures the data is up-to-date. Option A requires the aggregation to be performed every time the dashboard is loaded. Option C is less efficient than B because it filters after aggregation. Option D is not scalable for large datasets. Option E adds unnecessary complexity and potential performance bottlenecks with the stored procedure and temporary table.

NEW QUESTION # 30
A Data Analyst needs to add address details based on a customer's latitude and longitude to a customer sales database. The Analyst found a free Worldwide Address Data listing on the Snowflake Marketplace. The ACCOUNTADMIN placed the data set into a new database called ADDRESS_DATA. The Data Analyst needs to join the ADDRESS_DATA.OPENADDRESS table with the ORDERS table which is stored in the GLOBAL_DWH database. The combined data set needs to be created as a view. How can this be achieved?

A. Create a view in the GLOBAL_DWH database.
B. Create a view in the ADDRESS_DATA database.
C. Ask the ACCOUNTADMIN to grant the Data Analyst the IMPORTED_PRIVILEGES on the ADDRESS_DATA database and then create a view in the ADDRESS_DATA database.
D. Create a new schema called ENRICHED in the ADDRESS_DATA database and create this view in the ENRICHED schema.

Answer: A

Explanation:
This scenario highlights the rules governing Shared Databases and Cross-Database Joins in Snowflake.
When a user acquires data from the Snowflake Marketplace, it arrives in their account as a "Shared Database." A critical restriction of shared databases is that they are read-only. Users (including Data Analysts and even AccountAdmins) cannot create new objects-such as tables, schemas, or views-directly inside a database created from a share. Therefore, Options A, C, and D are architecturally impossible because they all involve trying to write a new view into the ADDRESS_DATA database.
To combine the shared data with internal data (like the ORDERS table in GLOBAL_DWH), the Analyst must create the view in a database where they have CREATE VIEW privileges and that is writeable. Since GLOBAL_DWH is an internal database owned by the organization, it is the appropriate location to host the logic that joins local data with external enrichment data.
Evaluating the Options:
* Option A and C are incorrect because you cannot create objects in a database created from a share.
* Option D is incorrect because while IMPORTED_PRIVILEGES are necessary to view the data, they do not grant the ability to create objects within the share.
* Option B is the 100% correct answer. By creating the view in the GLOBAL_DWH database (or any other local, non-shared database), the Analyst can successfully reference ADDRESS_DATA.
OPENADDRESS and GLOBAL_DWH.PUBLIC.ORDERS in a single SQL statement, effectively creating an enriched data layer for the organization.

NEW QUESTION # 31
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