A brief introduction to the course
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Snowflake DAA-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Data Ingestion and Data Preparation | 15%-20% | - 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)
- 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 a collection system to retrieve data
- 1. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
- 2. Synthetic Data Generation
- 3. Retrieve data from unstructured sources
- 4. Retrieve data from structured sources (CSV)
- Perform data discovery to identify what is needed from available datasets
- 1. Query tables to assess data elements and statistics maintained by Snowflake
- 2. Determine the level of data granularity required
- 3. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
- 4. Evaluate required transformations (table joins, set operations, ASOF JOINS)
- 5. Identify elements required for business goals using BI reports or SQL analysis
- 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
- Prepare data and load into Snowflake
- 1. Load files using Snowsight
- 2. Load data from external/internal stages into a table
|
| Topic 2: Data Transformation and Data Modeling | 22%-30% | - Design data models
- 1. Data vault models
- 2. Star schema design
- 3. Snowflake schema design
- Transform data using SQL
- 1. Common Table Expressions (CTEs)
- 2. QUALIFY clauses
- 3. PIVOT/UNPIVOT operations
- 4. Window functions
|
| Topic 3: Data Analysis | 30%-32% | - Perform advanced analytics using SQL
- 1. Aggregate functions
- 2. Snowflake-specific analytical features
- 3. Time-series analysis
|
| Topic 4: Data Presentation and Data Visualization | 28%-29% | - Integrate with BI tools
- 1. Tableau integration
- 2. Power BI integration
- 3. Other partner visualization tools
- Create dashboards
- 1. Present analytical results
- 2. Snowsight dashboards
|
Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:
1. 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) Table_b has a row-access policy defined.
B) A multi-cluster warehouse was used to run Query 1.
C) Table_b has more rows than Table_a.
D) The USE_CACHED_RESULT was set to FALSE before running Query 2.
2. What functionalities are available when a Snowflake worksheet is shared with other users? (Select TWO).
A) Whenever a user with permissions runs a worksheet, the existing version history of the worksheet will be overwritten.
B) If the worksheet is being edited, the collaborators will be able to see these edits in real-time.
C) Collaborators can share the worksheet across Snowflake accounts.
D) Users with edit permissions can view past versions of the worksheet.
E) If multiple users edit and run a shared worksheet at the same time, each run of the worksheet will create a new version.
3. A single variant data column table RAW_SOURCE has the following JSON records:

A Data Analyst needs to get the value of the "f" field and have it in a consumable, tabular format. Which query should be used to meet this requirement?
A) select src.events:f::number from raw_source src;
B) select data:events:f::number from raw_source;
C) select value:f::number from raw_source, lateral flatten( input => data:events );
D) select value:f::number from raw_source, lateral flatten( input => data );
4. A Data Analyst needs to create a custom filter called state in a Snowflake dashboard using a SQL query.
Which query will include this custom filter?
A) select avg(total_income), state from taxpayer_wages group by state having state = @state;
B) select avg(total_income), state from taxpayer_wages group by state having state = 'state';
C) select avg(total_income), state from taxpayer_wages group by state having state = state;
D) select avg(total_income), state from taxpayer_wages group by state having state = :state;
5. A Data Analyst executes a query in a Snowflake worksheet that returns the total number of daily sales, and the total amount for each sale. How can the Analyst check the distribution of the total amount, without running the query again?
A) Call the WIDTH_BUCKET function.
B) Go to Chart and select a histogram that includes the two variables.
C) Click on the column header in the results and review the histogram.
D) Go to Chart and select a bar chart that contains the two variables.
Solutions:
Question # 1 Answer: A | Question # 2 Answer: B,E | Question # 3 Answer: C | Question # 4 Answer: D | Question # 5 Answer: C |