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Snowflake DAA-C01 Exam Syllabus Topics:

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

Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:

1. A Data Analyst wants to use pandas code they have previously written to process a column of text and return multiple rows of parsed output for each input value. The Analyst wants to be able to join these results with other tables in a single transaction. Which type of extensibility feature should the Analyst use?

A) Vectorized User-Defined Table Function (UDTF)
B) User-Defined Function (UDF)
C) External function
D) Stored procedure

2. A Data Analyst is working with three tables:

Which query would return a list of all brokers, a count of the customers each broker has. and the total order amount of their customers (as shown below)?

A)

B)

C)

D)

3. A Data Analyst has a Parquet file stored in an Amazon S3 staging area. Which query will copy the data from the staged Parquet file into separate columns in the target table?

A) Option B
B) Option D
C) Option C
D) Option A

4. A Data Analyst is given access to a table of inventory transfers which includes the product details, fulfillment warehouse location, and destination warehouse location. Which type of visualization should be used to illustrate the most active product transfer routes?

A) Scorecard
B) Line chart
C) Scatterplot
D) Heat grid

5. There are two similarly-structured and sized tables, Table_a and Table_b, in a schema with data populated in both tables. A Data Analyst is running queries as part of a preliminary analysis of the data to check the MAX value of a numeric column named num which is present in both the tables:
* Query 1: SELECT MAX(num) FROM Table_a;
* Query 2: SELECT MAX(num) FROM Table_b;
After running the queries, the Analyst observed that Query 2 ran significantly slower than Query 1. Why is this occurring?

A) A multi-cluster warehouse was used to run Query 1.
B) The USE_CACHED_RESULT was set to FALSE before running Query 2.
C) Table_b has a row-access policy defined.
D) Table_b has more rows than Table_a.

Solutions:

Question # 1
Answer: A

Question # 2
Answer: A

Question # 3
Answer: C

Question # 4
Answer: D

Question # 5
Answer: C