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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Security, Governance, and Data Sharing	- Data sharing and governance 1. Data masking and policies 2. Secure data sharing - Access control and security 1. Authentication and encryption concepts 2. Role-based access control (RBAC)
Snowflake Architecture and Data Platform Fundamentals	- Data platform fundamentals 1. Separation of storage and compute 2. Data lifecycle in Snowflake - Snowflake architecture concepts 1. Virtual warehouses and scaling 2. Cloud services layer, compute layer, storage layer
Data Loading and Unloading	- Data ingestion methods 1. Continuous ingestion and Snowpipe concepts 2. COPY INTO and bulk loading - Data export 1. UNLOAD and external stages
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	- Modeling approaches in Snowflake 1. Star and snowflake schemas 2. Data normalization vs denormalization - Performance tuning 1. Warehouse sizing and auto-suspend/auto-resume 2. Clustering and pruning techniques

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
What actions are involved in data discovery to identify necessary elements from available datasets in Snowflake? (Select all that apply)

A. Running SQL queries on tables
B. Performing data mining techniques
C. Evaluating necessary transformations
D. Identifying missing data fields

Answer: A,C

Explanation:
Data discovery in Snowflake involves querying tables and evaluating required transformations for dataset refinement.

NEW QUESTION # 33
What actions are involved in performing general DML (Data Manipulation Language) operations in Snowflake? (Select all that apply)

A. Updating existing data
B. Merging data from multiple tables
C. Deleting data entirely
D. Inserting new data

Answer: A,C,D

Explanation:
General DML operations in Snowflake include inserting, updating, and deleting data.

NEW QUESTION # 34
A data analyst is tasked with creating a view in Snowflake that aggregates sales data by region. The underlying sales table is updated frequently. The analyst wants to optimize query performance and minimize the impact of these updates. Which of the following approaches would be the MOST suitable for creating the view?

A. Create a temporary table, populate it with the aggregated data, and query from the temporary table.
B. Create a recursive view to handle potential hierarchical region data.
C. Create a materialized view using CREATE MATERIALIZED VIEW statement.
D. Create a standard view using CREATE VIEW statement.
E. Create a secure view using CREATE SECURE VIEW statement.

Answer: C

Explanation:
A materialized view (Option B) is the most suitable because it pre-computes and stores the aggregated data. This improves query performance, especially for frequently accessed aggregated data. Since it's automatically refreshed when the underlying sales table is updated, it minimizes the impact of updates on query performance. Standard views are computed on the fly, negating performance benefits. Temporary tables require manual updates. Secure views focus on data security, not performance. Recursive views are for hierarchical data, not aggregation.

NEW QUESTION # 35
When using Snowflake functions, how do scalar functions differ from aggregate functions?

A. Aggregate functions return single values.
B. Scalar functions operate on entire tables.
C. Aggregate functions modify table structures.
D. Scalar functions work on groups of rows.

Answer: A

Explanation:
Aggregate functions return single values from groups of rows, distinguishing them from scalar functions.

NEW QUESTION # 36
You are tasked with analyzing website traffic data stored in a Snowflake table named 'page_views'. The table has columns 'user_id' (INT), 'page_url (VARCHAR), and 'view_time' (TIMESTAMP N T Z). You need to identify users who are likely bots based on an unusually high number of page views within a short period. Specifically, you want to flag users who have more than 100 page views within any 5-minute window Which of the following queries is the MOST efficient and accurate way to achieve this?

Answer: C

Explanation:
Option C is the most accurate and efficient. It correctly calculates the number of views within a 5-minute window for each user using the 'DATE DIFF function and a window function. It then filters to only include users who exceed 100 views in any of those windows, and ensures each user is only counted once with the DISTINCT keyword. Option A is incorrect because it only looks at total page views across the entire dataset. Option B is syntactically incorrect and doesn't implement the time window. Option D is truncating by minute and then groups across that resulting in incorrect aggregation. The correct function is 'DATE DIFF, not 'DATE TRUNC and the logic for window is incorrect. Option E will result in errors as TUMBLE_START requires view_time to be timestamp_ltz or timestamp_tz data type.

NEW QUESTION # 37
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