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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q142-Q147):

NEW QUESTION # 142
Your company needs to comply with GDPR regulations, and you're tasked with implementing data masking to protect sensitive customer information. Specifically, you need to mask email addresses in a 'CUSTOMERS' table during data preparation. Which of the following approaches, utilizing Snowflake's system functions and data masking capabilities, offers the MOST secure and flexible solution, while allowing authorized users to potentially unmask the data if necessary?

A. Create a view that uses 'REGEXP REPLACE()' function to redact portions of the email address. Grant access to the view instead of the table.
B. Create a masking policy that uses 'CASE statement based on IS_ROLE_IN_SESSION() function. When the role is Analyst role it should display original value, if not then it must display null value. Apply this policy to the EMAIL ADDRESS column in the CUSTOMERS table.
C. Use the 'MD5()' function to encrypt the email addresses. Authorized users can decrypt the MD5 hash using the appropriate key.
D. Use the 'SHA2()' function with a salt to hash the email addresses. Authorized users can reverse the hash using the correct salt.
E. Create a masking policy using the function to redact portions of the email address. Apply this policy to the EMAIL_ADDRESS column in the CUSTOMERS table.

Answer: B

Explanation:
Option C provides the best combination of security and flexibility. Snowflake's masking policies, combined with functions like , allow for dynamic masking based on user roles. Authorized users (e.g., those in an 'analyst' role) can see the original email address, while others see a masked version. Options A and D are not recommended as hashing/encryption functions like SHA2() and MD5() are not easily reversible and are not the intended use case for data masking policies. Option B offers masking with REGEXP_REPLACE but no mechanism to unmask with proper permissions. Option E creates a view, which is not centrally managed or scalable solution.

NEW QUESTION # 143
How do materialized views differ from secure views in data analysis?

A. Secure views precompute data, unlike materialized views.
B. Materialized views restrict data access for improved security.
C. Materialized views offer enhanced data security while allowing selective data access.
D. Secure views provide precomputed snapshots, unlike materialized views.

Answer: A

Explanation:
Secure views offer enhanced data security without precomputing data, distinguishing them from materialized views.

NEW QUESTION # 144
How do secure views enhance data analysis practices?

A. Secure views limit access to data, hindering analysis.
B. Secure views prevent the creation of materialized views.
C. They improve query performance but don't impact data security.
D. Secure views provide enhanced data security while enabling selective data access.

Answer: D

Explanation:
Secure views offer enhanced data security by allowing selective data access, benefiting analysis while maintaining security.

NEW QUESTION # 145
Which action is crucial for identifying demographics and relationships during diagnostic analysis?
(Select all that apply)

A. Considering relationships among data variables
B. Analyzing isolated anomalies without considering relationships
C. Examining demographic variations linked to anomalies
D. Ignoring statistical trends for focused analysis

Answer: A,C

Explanation:
Analyzing demographic variations and considering relationships are crucial in identifying anomalies during diagnostic analysis.

NEW QUESTION # 146
A data analyst is experiencing slow query performance when joining two large tables, 'SALES' (1 billion rows) and 'CUSTOMERS' (10 million rows), on 'CUSTOMER ID. The 'SALES' table is frequently updated. The following query is used: SELECT s. , c. FROM SALES s JOIN CUSTOMERS c ON s.CUSTOMER_lD = c.CUSTOMER_lD WHERE s.SALE_DATE DATEADD(day, -30, CURRENT DATE()); Which of the following strategies would MOST effectively improve the query performance, assuming you have appropriate privileges to alter objects and cost is a concern?

A. Create a search optimization on 'SALES.CUSTOMER and 'CUSTOMERS.CUSTOMER ID.
B. Create a materialized view joining the two tables and filter on 'SALE_DATE. Refresh the materialized view daily.
C. Create a 'SALES LAST 30 DAYS' table containing only the last 30 days of sales, and join that table to the 'CUSTOMERS table. Refresh the 'SALES LAST 30 DAYS daily.
D. Create a standard index on 'SALES.CUSTOMER ID and 'CUSTOMERS.CUSTOMER ID.
E. Create a cluster key on 'SALES' table on 'CUSTOMER_ID' and re-cluster the table after data loading.

Answer: C

Explanation:
Creating 'SALES LAST 30 DAYS' table and joining is the most effective strategy because it reduces the size of the SALES table significantly before the join operation. Materialized views (A) are generally effective, but the high update frequency of the SALES table could lead to significant overhead in materialized view maintenance. Standard indexes (B) are not supported in Snowflake. Search optimization (C) may not be suitable as the could have high cardinality. Clustering (D) could improve performance, but the benefits might not outweigh the cost of reclustering after frequent data loading, and it does not limit the initial size of the join.

NEW QUESTION # 147
......

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Keith Bell Keith Bell

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