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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Time-series analysis
  • 2. Aggregate functions
  • 3. Snowflake-specific analytical features
Data Ingestion and Data Preparation15%-20%- Perform data discovery to identify what is needed from available datasets
  • 1. Query tables to assess data elements and statistics maintained by Snowflake
  • 2. Evaluate required transformations (table joins, set operations, ASOF JOINS)
  • 3. Determine the level of data granularity required
  • 4. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
  • 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
- Use a collection system to retrieve data
  • 1. Retrieve data from unstructured sources
  • 2. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
  • 3. Retrieve data from structured sources (CSV)
  • 4. Synthetic Data Generation
- 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
- 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)
Data Presentation and Data Visualization28%-29%- Create dashboards
  • 1. Snowsight dashboards
  • 2. Present analytical results
- Integrate with BI tools
  • 1. Tableau integration
  • 2. Power BI integration
  • 3. Other partner visualization tools
Data Transformation and Data Modeling22%-30%- Design data models
  • 1. Star schema design
  • 2. Data vault models
  • 3. Snowflake schema design
- Transform data using SQL
  • 1. QUALIFY clauses
  • 2. Common Table Expressions (CTEs)
  • 3. Window functions
  • 4. PIVOT/UNPIVOT operations

Snowflake SnowPro Advanced: Data Analyst Certification Sample Questions:

Question 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


Question 2

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.


Question 3

When sharing views across accounts, which function should be used on the row access policy for the secure views to give users access to rows in a base table?

A. CURRENT_AVAILABLE_ROLES
B. CURRENT_ACCOUNT
C. CURRENT_ROLE
D. CURRENT_USER


Question 4

Table TB_A with column COL_B contains an ARRAY. Which statement will select the last element of the ARRAY?

A. SELECT LAST_VALUE(COL_B) FROM TB_A;
B. SELECT COL_B[-1] FROM TB_A;
C. SELECT GET(COL_B, ARRAY_SIZE(COL_B)-1) FROM TB_A;
D. SELECT COL_B[ARRAY_SIZE(COL_B)] FROM TB_A;


Question 5

A scorecard tile on a Snowsight dashboard shows a comparison between the industry average employee age (which is 34) and a company's average employee age (which is 38). The scorecard tile looks like this:
Comparison with industry average

How should this tile be interpreted?

A. 38 is the value and 12% is the standard deviation.
B. 38 is the comparison and 12% is the average age change in the last year.
C. 38 is the value and 12% is the percent difference from the comparison.
D. 38 is the comparison and 12% is the projected industry growth.


Solutions:

Question 1
Answer: A
Question 2
Answer: C
Question 3
Answer: B
Question 4
Answer: C
Question 5
Answer: C

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