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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Security, Governance, and Data Sharing	- Access control and security 1. Authentication and encryption concepts 2. Role-based access control (RBAC) - Data sharing and governance 1. Secure data sharing 2. Data masking and policies
Topic 2: 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. Separation of storage and compute 2. Data lifecycle in Snowflake
Topic 3: Data Loading and Unloading	- Data ingestion methods 1. COPY INTO and bulk loading 2. Continuous ingestion and Snowpipe concepts - Data export 1. UNLOAD and external stages
Topic 4: Data Transformation and Analysis	- SQL-based transformations 1. Semi-structured data (VARIANT, JSON, XML) 2. Joins, aggregations, window functions - Analytical workloads 1. Materialized views and caching 2. Query optimization for analytics
Topic 5: Data Modeling and Performance Optimization	- Modeling approaches in Snowflake 1. Data normalization vs denormalization 2. Star and snowflake schemas - 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 (Q31-Q36):

NEW QUESTION # 31
You have a Snowflake table 'customer data' containing personally identifiable information (PII) such as customer names and addresses. You need to create a de-identified version of this table for analytical purposes, replacing the names and addresses with synthetic data, while preserving referential integrity with other tables that use the original customer IDs. Which combination of Snowflake features and techniques would you use to achieve this securely and efficiently?

A. Export the 'customer_data' table to a local file, de-identify the data using an external scripting language (e.g., Python), and then import the de-identified data back into Snowflake.
B. Create a view on top of the original table using Snowflake's masking policies and row-level security, allowing only specific users to see the de-identified data.
C. Use Snowflake's data masking policies to dynamically mask the PII columns during query execution.
D. Use Snowflake's external functions to call an external service that provides de-identification capabilities.
E. Create a new table with the de-identified data using a combination of Snowflake's HASH function for customer IDs, and a user-defined function (UDF) to generate synthetic names and addresses. Use secure data sharing to provide access to the de-identified table.

Answer: D,E

Explanation:
Options B and D are the best choices. Option B directly addresses the need for a de-identified table by generating synthetic data and using HASH to preserve referential integrity. Secure data sharing provides controlled access. Option D uses external functions to leverage specialized de-identification services. Options A and E are useful for dynamic masking, but do not create a separate de-identified dataset and may still expose PII to authorized users. Option C involves exporting data, which increases the risk of data leakage and is less efficient.

NEW QUESTION # 32
In Snowflake, how does Time Travel feature assist in data retrieval and analysis?

A. Provides real-time data updates
B. Enables querying data as of a specific point in time
C. Accelerates query performance significantly
D. Limits data access for specific user roles

Answer: B

Explanation:
The Time Travel feature allows querying data as of specific timestamps, enabling historical data retrieval and analysis at various points in time.

NEW QUESTION # 33
What critical role do user-defined functions (UDFs) play in SQL for advanced data analysis?

A. UDFs only handle basic data manipulation tasks.
B. They enable customized operations on data, extending SQL functionalities.
C. UDFs are limited to specific data types.
D. UDFs hinder query optimization in SQL.

Answer: B

Explanation:
UDFs extend SQL functionalities by enabling customized operations on data, going beyond standard SQL capabilities.

NEW QUESTION # 34
A company ingests sensor data into a Snowflake table named READINGS with columns (VARCHAR), 'reading_time' (TIMESTAMP NTZ), and 'raw_value' (VARCHAR). The 'raw_value' column contains numeric data represented as strings, but sometimes includes non-numeric characters (e.g., '123.45', 'N/A', '500'). You need to calculate the average of the numeric raw_value' readings for each within the last hour, excluding invalid readings. Which of the following Snowflake SQL statements will correctly accomplish this, handling potential conversion errors and filtering for valid data?

A. SELECT sensor_id, FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1 , AND TRY_TO IS NOT NULL GROUP BY sensor_id;
B. SELECT sensor_id, FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT _ TIMESTAMP()) GROUP BY sensor id;
C. SELECT sensor_id, 'N/A'))) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1 , CURRENT TIMESTAMP()) GROUP BY sensor_id;
D. SELECT sensor_id, AVG(CASE WHEN THEN ELSE NULL END) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT TIMESTAMP()) GROUP BY sensor_id;
E. SELECT sensor_id, raw_value, NULL))) FROM SENSOR_READINGS WHERE reading_time DATEADD(hour, -1, CURRENT TIMESTAMP()) GROUP BY sensor_id;

Answer: A

Explanation:
Option B is the correct answer because 'TRY TO NUMBER attempts to convert the 'raw_value' to a number, returning NULL if the conversion fails. The 'AND TRY_TO_NUMBER(raw_value) IS NOT NULL' clause then filters out these NULL values, ensuring only valid numeric readings are included in the average calculation. Option A will throw an error if it encounters a non-numeric value. Option C, while functionally correct, utilizes which can be less reliable for specific locale formats compared to Option D is unnecessarily complex and less readable. Option E only handles 'N/A', not other potential invalid values.

NEW QUESTION # 35
Which Snowflake feature allows users to encapsulate a series of SQL statements into a reusable database object, facilitating modular code development?

A. Common Table Expressions (CTE)
B. Stored procedure
C. User-Defined Function (UDF)
D. Materialized view

Answer: B

NEW QUESTION # 36
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