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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q30-Q35):

NEW QUESTION # 30
Your organization receives daily updates to a product catalog in Avro format. The Avro schema evolves frequently, adding and removing fields. You need to ingest these Avro files into Snowflake. Which of the following strategies will BEST handle schema evolution while minimizing data loading complexity?

A. Create a new Snowflake table for each Avro schema version. Maintain a view that unions all tables to provide a consistent interface.
B. Define a fixed Snowflake table schema with a limited set of commonly used fields. Ignore any additional fields in the Avro files during loading.
C. Load the Avro files into a VARIANT column in Snowflake and use SQL to extract the required fields. Periodically update the table schema as the Avro schema evolves.
D. Load the Avro files into a CSV format after flattening the Avro Schema.
E. Use Snowflake's Schema Detection feature with the COPY command into a table with a VARIANT column. Then, use the 'LATERAL FLATTEN' function to extract the fields and load them into a relational table with the correct data types. Automate the schema creation/alteration of the relational table based on the inferred schema from the VARIANT column.

Answer: E

Explanation:
Option D is the best approach. Loading the Avro data into a VARIANT column first allows you to capture all the data, regardless of schema evolution. Using the 'LATERAL FLATTEN' function allows extraction of fields. Utilizing Snowflake's schema detection further automates the process. Option A requires manual schema updates, which is less scalable. Option B creates a management overhead with multiple tables and views. Option C loses data. Option E requires external flattening which creates additional complexity.

NEW QUESTION # 31
What is a critical consideration when using data shares in Snowflake to join data with existing datasets?

A. The visual appeal of the data
B. The number of likes on the Snowflake's social media page
C. Ensuring data schema compatibility
D. The brand of the data visualization tool

Answer: C

NEW QUESTION # 32
You are developing a Snowflake stored procedure that uses an external Python library (e.g., scikit-learn for machine learning). The library is not natively available within Snowflake's Python environment. What is the correct process to include and utilize this external library within your stored procedure?

A. Create a Snowflake stage, upload the library's '.whl' file to the stage, and then use the 'CREATE PROCEDURE statement with the 'IMPORTS' clause to specify the stage and .whl' file. Snowflake will then install the library during procedure creation.
B. Include the source code of the library directly within the stored procedure's Python code.
C. Use the 'pip install' command within the stored procedure's Python code to install the library from PyPl during each execution of the procedure.
D. Simply import the library in your Python code within the stored procedure. Snowflake automatically downloads and installs any missing libraries from PyPl when the procedure is executed.
E. Upload the library using the Snowflake web interface, so Snowflake will know which library it should be using.

Answer: A

Explanation:
Option B is the correct approach. Snowflake uses stages and the 'IMPORTS' clause to manage external dependencies for Python stored procedures. You must upload the .whl' file of the library to a stage and then reference it in the 'CREATE PROCEDURE' statement. This ensures that the library is available when the procedure is executed. Option A is incorrect because Snowflake does not automatically download libraries from PyPl. Option C is incorrect because you cannot execute shell commands like 'pip install' within a stored procedure. Option D is generally impractical for larger libraries, and Option E isn't a valid approach.

NEW QUESTION # 33
How does leveraging partition pruning optimize query performance in Snowflake?

A. Filters unnecessary partitions during query execution
B. Increases storage requirements for optimized query access
C. Limits query complexity and optimization possibilities
D. Reduces data accessibility across multiple warehouses

Answer: A

Explanation:
Partition pruning in Snowflake filters unnecessary partitions during query execution, enhancing query performance by minimizing the amount of data scanned.

NEW QUESTION # 34
How do materialized views differ from regular views in terms of data access and storage?

A. Regular views offer better storage optimization compared to materialized views.
B. Materialized views restrict data access for improved security.
C. Regular views enhance data accessibility more effectively than materialized views.
D. Materialized views provide precomputed snapshots, unlike regular views.

Answer: D

Explanation:
Materialized views provide precomputed snapshots, differentiating them from regular views.

NEW QUESTION # 35
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