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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Transformation and Data Modeling	22%-30%	- Transform data using SQL 1. PIVOT/UNPIVOT operations 2. Common Table Expressions (CTEs) 3. Window functions 4. QUALIFY clauses - Design data models 1. Data vault models 2. Snowflake schema design 3. Star schema design
Topic 2: Data Ingestion and Data Preparation	15%-20%	- Implement data processing solutions 1. Cleanse, conform, and enrich data 2. Use logging and monitoring solutions (auditing, data lineage) 3. Automate and implement data pipelines (scheduling) 4. Respond to processing failures - Use best practice considerations relating to data integrity structures 1. Implement constraints 2. Define primary keys for tables 3. Perform table joins between parent/child tables - Prepare data and load into Snowflake 1. Load files using Snowsight 2. Load data from external/internal stages into a table - Enrich data by identifying and accessing relevant data from the Snowflake Marketplace 1. Find external data sets that correlate with available data 2. Create tables and views 3. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) - Use a collection system to retrieve data 1. Retrieve data from structured sources (CSV) 2. Retrieve data from unstructured sources 3. Synthetic Data Generation 4. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) - Perform data discovery to identify what is needed from available datasets 1. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 2. Evaluate required transformations (table joins, set operations, ASOF JOINS) 3. Query tables to assess data elements and statistics maintained by Snowflake 4. Determine the level of data granularity required 5. Identify elements required for business goals using BI reports or SQL analysis
Topic 3: Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Time-series analysis 2. Snowflake-specific analytical features 3. Aggregate functions
Topic 4: Data Presentation and Data Visualization	28%-29%	- Create dashboards 1. Present analytical results 2. Snowsight dashboards - Integrate with BI tools 1. Tableau integration 2. Other partner visualization tools 3. Power BI integration

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q49-Q54):

NEW QUESTION # 49
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. Use the 'SHA2()' function with a salt to hash the email addresses. Authorized users can reverse the hash using the correct salt.
B. Use the 'MD5()' function to encrypt the email addresses. Authorized users can decrypt the MD5 hash using the appropriate key.
C. 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.
D. 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.
E. Create a view that uses 'REGEXP REPLACE()' function to redact portions of the email address. Grant access to the view instead of the table.

Answer: C

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 # 50
The following code is run:

Then this statement is executed:

What will be the output of this statement?

Answer: C

Explanation:
To determine the correct output, a Data Analyst must understand the behavior of the ILIKE operator, wildcard characters, and the ESCAPE clause in Snowflake.
1. Pattern Matching with ILIKE: The ILIKE operator performs case-insensitive pattern matching. The provided pattern is 'p%

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