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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Data Modeling and Performance Optimization	- Performance tuning 1. Clustering and pruning techniques 2. Warehouse sizing and auto-suspend/auto-resume - Modeling approaches in Snowflake 1. Data normalization vs denormalization 2. Star and snowflake schemas
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)
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
Snowflake Architecture and Data Platform Fundamentals	- Snowflake architecture concepts 1. Virtual warehouses and scaling 2. Cloud services layer, compute layer, storage layer - Data platform fundamentals 1. Data lifecycle in Snowflake 2. Separation of storage and compute
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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
How do row access policies and Dynamic Data Masking affect the creation and maintenance of reports and dashboards?

A. Dynamic Data Masking doesn't influence dashboard creation.
B. They offer unrestricted data visibility for all users.
C. Both policies restrict data visibility for better security.
D. Row access policies limit data visibility based on user privileges.

Answer: D

Explanation:
Row access policies restrict data visibility based on user privileges, ensuring better security in creation and maintenance of reports and dashboards.

NEW QUESTION # 66
You have a Snowflake table called 'PRODUCT SALES' with columns 'PRODUCT ID (INT), 'SALE DATE' (DATE), and 'SALES AMOUNT' You want to implement a data integrity rule to prevent duplicate records based on 'PRODUCT ID and 'SALE DATE. Which of the following methods provides the most effective way to achieve this in Snowflake, and why?

A. Implement data validation within your ETL pipeline before loading data into Snowflake to prevent duplicates from entering the table. This approach keeps the table clean from the start.
B. Create a view that filters out duplicate records using 'ROW NUMBER()' and partitioning by 'PRODUCT ID and 'SALE DATE. This guarantees data integrity during query execution.
C. Create a user defined function (UDF) that checks for the existance of data before insert. If data already exist, don't insert it.
D. Add a composite UNIQUE constraint on 'PRODUCT ID' and 'SALE DATE. Snowflake will automatically prevent the insertion of duplicate rows during data loading or insertion.
E. Create a stored procedure that runs periodically to identify and delete duplicate records based on 'PRODUCT and 'SALE DATE'. This approach fixes integrity issues reactively.

Answer: A,D

Explanation:
Options C and D provide the most effective methods. A composite UNIQUE constraint directly prevents duplicate insertions at the table level. Validating in the ETL pipeline (D) prevents duplicates before they even reach the database. A view (A) only masks the issue, and a stored procedure (B) is reactive and doesn't prevent duplicates from being inserted in the first place. A UDF could be helpful but is not the BEST option for this scenario.

NEW QUESTION # 67
How do row access policies and Dynamic Data Masking impact the creation of dashboards concerning data visibility and security?

A. Dynamic Data Masking doesn't influence dashboard creation.
B. Both policies restrict data visibility for better security.
C. They enhance data visibility for all users without limitations.
D. Row access policies limit data visibility based on user privileges.

Answer: D

Explanation:
Row access policies limit data visibility based on user privileges, ensuring better security in dashboard creation.

NEW QUESTION # 68
A Data Analyst executes a complex query. Which query will allow the Analyst to access the results a second time?

A. SELECT LAST_QUERY_ID(-1);
B. DESC RESULT LAST_QUERY_ID();
C. SELECT * FROM TABLE(RESULT_SCAN(LAST_QUERY_ID()));
D. SELECT * FROM TABLE(INFORMATION_SCHEMA.QUERY_HISTORY());

Answer: C

Explanation:
Snowflake provides a powerful feature called Result Caching. When a query is executed, the results are persisted for 24 hours. To access the result set of a previously executed query without re-running the heavy computation (and thus saving credits), a Data Analyst can use the RESULT_SCAN table function.
The function RESULT_SCAN requires a Query ID as its input. The most common way to programmatically retrieve the ID of the most recently executed statement in the current session is by using the LAST_QUERY_ID() function. By wrapping this in the TABLE() operator, Snowflake treats the cached results as a virtual table that can be queried, filtered, or even joined with other tables.
Evaluating the Options:
* Option B is incorrect because QUERY_HISTORY returns metadata about queries (like start time, user, and status) but does not return the actual data records produced by those queries.
* Option C is incorrect as DESC (Describe) is used to see the columns and data types of an object, not to fetch its row data.
* Option D is incorrect; while LAST_QUERY_ID(-1) might successfully retrieve a string representing a previous ID in some contexts, it does not actually "access the results" or display the data.
* Option A is the 100% correct syntax. It is the standard method used by Data Analysts to perform post- processing on a large result set or to recover a result set if the worksheet was accidentally cleared. This is a key part of the Data Analysis domain, specifically regarding performance optimization and the utilization of Snowflake's unique caching layers.

NEW QUESTION # 69
How do aggregate functions differ from window functions in Snowflake?

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

Answer: A,D

Explanation:
Aggregate functions operate on groups of rows and return single values, distinguishing them from window functions.

NEW QUESTION # 70
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