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| Section | Objectives |
|---|---|
| Security, Governance, and Data Sharing | - Access control and security
|
| Snowflake Architecture and Data Platform Fundamentals | - Snowflake architecture concepts
|
| Data Modeling and Performance Optimization | - Modeling approaches in Snowflake
|
| Data Loading and Unloading | - Data ingestion methods
|
| Data Transformation and Analysis | - Analytical workloads
|
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NEW QUESTION # 20
You have identified a valuable dataset on the Snowflake Marketplace related to weather patterns. To consume this data, you perform the following actions: 1. You request and receive the data share from the provider. 2. You create a database named 'WEATHER DB' from the share. Now you want to create a secure view named 'DAILY WEATHER SUMMARY in your own database 'ANALYTICS DB.PUBLIC', which joins your internal sales data C ANALYTICS DB.PUBLIC.SALES) with the weather data from the provider's 'WEATHER DB.WEATHER SCHEMA.DAILY WEATHER' table. You only want to expose specific columns from both tables in your view to minimize data exposure. Which of the following steps are required to ensure this secure and functional integration?
Answer: D
Explanation:
Option D is the correct answer. When consuming data from a Snowflake Marketplace data share, you do not need to grant privileges to the provider on your internal data (Option A is incorrect). Option B is incorrect because creating views and acessing tables requires proper schema names. Option C is incorrect because outbound shares are not needed to consume marketplace data and access control is managed within the consumer account. Option E is incorrect because enabling access control policies on a warehouse is irrelevant to the task of creating a secure view that joins internal data with shared data.
NEW QUESTION # 21
What considerations should be taken into account when choosing between a dimensional model and a flattened data set for BI requirements in Snowflake? (Select all that apply)
Answer: B,C,D
Explanation:
Considerations include data redundancy, normalization needs, query complexity, performance expectations, flexibility, and ease of maintenance when choosing the appropriate data structure for BI requirements.
NEW QUESTION # 22
How do materialized views differ from regular views in the context of data analysis?
Answer: C
Explanation:
Materialized views offer precomputed snapshots, differentiating them from regular views.
NEW QUESTION # 23
How can incorporating visualizations in reports and dashboards facilitate better data comprehension and analysis for business use scenarios?
Answer: B
Explanation:
Visualizations enhance data comprehension, aiding effective analysis in business use scenarios.
NEW QUESTION # 24
You're tasked with building a data model in Snowflake for a retail company. The company has data on products ('PRODUCTS), sales transactions ('SALES), and customer demographics ('CUSTOMERS). You need to design a star schema to support efficient analysis of sales performance by product category and customer segment. Which of the following statements accurately describes the recommended table design and relationships within the star schema for this scenario?
Answer: C,D
Explanation:
Option B accurately describes a star schema design with a central fact table (SALES FACT) and dimension tables for products and customers. The fact table contains the numerical measures, and the dimension tables provide the context for analysis. Option D correctly describes Snowflake design, which is an extension of Star schema. Option A is generally not recommended because it leads to data redundancy and potential inconsistencies. Option C is not scalable or maintainable as the product range expands. Option E would result in slower query performance compared to a star schema design.
NEW QUESTION # 25
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