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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
When sharing views across accounts, which function should be used on the row access policy for the secure views to give users access to rows in a base table?

Answer: D

Explanation:
In the context of Snowflake Data Sharing, the security model changes because the users and roles accessing the data do not exist in the provider's account. When a provider shares a Secure View that is protected by a Row Access Policy (RAP), standard functions like CURRENT_ROLE or CURRENT_USER are often insufficient or return different values than expected because they reflect the consumer's local environment.
To effectively filter data for different consumers within a single shared table, the CURRENT_ACCOUNT() function is the primary tool. This function returns the unique account identifier of the system currently accessing the data. By including CURRENT_ACCOUNT() in the row access policy logic (e.g., WHERE shared_account_id = CURRENT_ACCOUNT()), the data provider can ensure that each consumer account only sees the specific subset of rows intended for them.
Evaluating the Options:
* Option A and D: CURRENT_ROLE and CURRENT_AVAILABLE_ROLES are generally used for internal account security. When data is shared, the roles in the consumer account do not map to the provider's roles, making these functions unreliable for cross-account row-level filtering.
* Option C: CURRENT_USER is likewise restricted to the local account's user metadata.
* Option B: Is the 100% correct answer. It is the specific system function designed to identify the consumer account in a data-sharing relationship, allowing for robust multi-tenant data protection. This is a vital skill for the Data Governance and Security domain, ensuring that "Secure Data Sharing" remains truly secure.


NEW QUESTION # 61
What functions should a Data Analyst use to run descriptive analytics on a data set? (Select TWO).

Answer: A,E

Explanation:
Descriptive analytics is the process of using historical data to understand "what happened." This typically involves summarizing large datasets into interpretable chunks using central tendency, dispersion, and frequency measures.
AVG (Average) is a cornerstone of descriptive statistics. It provides the arithmetic mean of a numeric column, allowing an analyst to understand the "typical" value within a dataset (e.g., Average Order Value).
APPROX_COUNT_DISTINCT is a descriptive tool used to understand the volume of unique entities within a dataset (e.g., "How many unique customers visited the site?"). Similar to HLL mentioned earlier, this function provides a fast summary of data volume and variety, which is a primary goal of the descriptive phase of analysis.
Evaluating the Options:
* Options A and B (REGR_INTERCEPT and REGR_SLOPE) are used for linear regression. These fall under Predictive Analytics, as they are used to model relationships and predict future outcomes, rather than just describing current data.
* Option C (ROW_NUMBER) is a window function used for data ranking and ordering, but it does not provide a descriptive summary of the dataset's characteristics.
* Options D and E are correct because they provide summary statistics (mean and cardinality) that define the "state" of the data, which is the definition of descriptive analytics.


NEW QUESTION # 62
When choosing between using a dimensional model and a flattened dataset for BI requirements in Snowflake, what considerations impact the final decision? (Select all that apply)

Answer: B,C

Explanation:
Considerations such as query performance expectations and data denormalization needs influence the choice between a dimensional model and a flattened dataset, affecting query optimization and data structure suitability for BI purposes.


NEW QUESTION # 63
A data analyst needs to process a large JSON payload stored in a VARIANT column named 'payload' in a table called 'raw events' The payload contains an array of user sessions, each with potentially different attributes. Each session object in the array has a 'sessionld' , 'userld' , and an array of 'eventS. The events array contains objects with 'eventType' and 'timestamp'. The analyst wants to use a table function to flatten this nested structure into a relational format for easier analysis. Which approach is most efficient and correct for extracting and transforming this data?

Answer: C

Explanation:
Option A is the most efficient and Snowflake-native approach. LATERAL FLATTEN is optimized for handling nested data structures within Snowflake. While other options might work, they introduce overhead (UDF execution), are less efficient (temporary tables and complex SQL), or rely on external frameworks (Snowpark), making them less suitable for this scenario. Specifying the path ensures specific fields are targeted, avoiding unnecessary processing of irrelevant data. LATERAL flatten allows you to join the output of a table function with each row of the input table. This is essential to maintain the context (e.g., userId) from the outer table.


NEW QUESTION # 64
When identifying and accessing relevant data from the Snowflake Marketplace, what challenges might arise in correlating external datasets with available data?

Answer: A

Explanation:
Correlating external datasets might face challenges due to data format compatibility issues between different sources.


NEW QUESTION # 65
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