Snowflake SnowPro Advanced: Data Analyst Certification : DAA-C01 exam dump

  • Exam Code: DAA-C01
  • Exam Name: SnowPro Advanced: Data Analyst Certification Exam
  • Updated: Aug 26, 2026
  • Q&As: 67 Questions and Answers

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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Ingestion and Data Preparation15%-20%- Implement data processing solutions
  • 1. Cleanse, conform, and enrich data
  • 2. Automate and implement data pipelines (scheduling)
  • 3. Use logging and monitoring solutions (auditing, data lineage)
  • 4. Respond to processing failures
- 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. Query tables to assess data elements and statistics maintained by Snowflake
  • 3. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
  • 4. Evaluate required transformations (table joins, set operations, ASOF JOINS)
  • 5. Determine the level of data granularity required
- 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. 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 semi-structured sources (Parquet, Avro, ORC, JSON, XML)
  • 2. Retrieve data from structured sources (CSV)
  • 3. Synthetic Data Generation
  • 4. Retrieve data from unstructured sources
- Use best practice considerations relating to data integrity structures
  • 1. Define primary keys for tables
  • 2. Implement constraints
  • 3. Perform table joins between parent/child tables
Topic 2: Data Transformation and Data Modeling22%-30%- Design data models
  • 1. Snowflake schema design
  • 2. Data vault models
  • 3. Star schema design
- Transform data using SQL
  • 1. PIVOT/UNPIVOT operations
  • 2. Window functions
  • 3. QUALIFY clauses
  • 4. Common Table Expressions (CTEs)
Topic 3: Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Time-series analysis
  • 2. Snowflake-specific analytical features
  • 3. Aggregate functions
Topic 4: Data Presentation and Data Visualization28%-29%- Create dashboards
  • 1. Snowsight dashboards
  • 2. Present analytical results
- Integrate with BI tools
  • 1. Power BI integration
  • 2. Tableau integration
  • 3. Other partner visualization tools

Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:

1. A Data Analyst created a SQL statement that updates a table used for reporting. The Analyst now wants to automate the execution of that SQL.
The Analyst decides to use a task for this along with a stream called MYSTREAM on the source table so only they can update the table if there is new data.
Which statement will create a task that will execute when there is new data in MYSTREAM without having to be executed manually?

A)

B)

C)

D)


2. The following code is run:

Then this statement is executed:

What will be the output of this statement?

A)

B)

C)

D)


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


4. While loading data into Snowflake using named file formats, a file format defined in which location has precedence?

A) The stage definition
B) The table definition
C) The COPY INTO statement
D) The schema definition


5. Why would a Data Analyst use a dimensional model rather than a single flat table to meet BI requirements for a virtual warehouse? (Select TWO).

A) Snowflake generally performs better with dimensional modelling.
B) Dimensional modelling will save on storage space since it is denormalized.
C) Combining facts and dimensions in a single flat table limits the scalability and flexibility.
D) Dimensional modelling will improve query performance over a single table.
E) Dimensions and facts allow power users to run ad-hoc analyses.


Solutions:

Question # 1
Answer: B
Question # 2
Answer: D
Question # 3
Answer: C
Question # 4
Answer: C
Question # 5
Answer: C,E

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