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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Aggregate functions
  • 2. Snowflake-specific analytical features
  • 3. Time-series analysis
Topic 2: Data Presentation and Data Visualization28%-29%- Integrate with BI tools
  • 1. Power BI integration
  • 2. Tableau integration
  • 3. Other partner visualization tools
- Create dashboards
  • 1. Snowsight dashboards
  • 2. Present analytical results
Topic 3: Data Ingestion and Data Preparation15%-20%- 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
- Perform data discovery to identify what is needed from available datasets
  • 1. Determine the level of data granularity required
  • 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. Evaluate required transformations (table joins, set operations, ASOF JOINS)
  • 5. Query tables to assess data elements and statistics maintained by Snowflake
- 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
- Prepare data and load into Snowflake
  • 1. Load data from external/internal stages into a table
  • 2. Load files using Snowsight
- 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
- Implement data processing solutions
  • 1. Use logging and monitoring solutions (auditing, data lineage)
  • 2. Cleanse, conform, and enrich data
  • 3. Automate and implement data pipelines (scheduling)
  • 4. Respond to processing failures
Topic 4: Data Transformation and Data Modeling22%-30%- Transform data using SQL
  • 1. PIVOT/UNPIVOT operations
  • 2. QUALIFY clauses
  • 3. Window functions
  • 4. Common Table Expressions (CTEs)
- Design data models
  • 1. Snowflake schema design
  • 2. Star schema design
  • 3. Data vault models

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q52-Q57):

NEW QUESTION # 52
What will be the output of this query?
SELECT 100::FLOAT * 20.78::INTEGER || ' Square Feet';

Answer: D

Explanation:
This question tests a Data Analyst's understanding of explicit casting and rounding behavior within Snowflake. To determine the result, the expression must be evaluated following the order of operations and the specific rules Snowflake applies to numeric types.
Step 1: The Integer Cast
The expression 20.78::INTEGER is the first critical part. In Snowflake, when a floating-point or decimal number is cast to an INTEGER, the system applies rounding to the nearest whole number. Because the decimal portion (0.78) is 0.5 or greater, the value 20.78 is rounded up to 21. If the value had been 20.49, it would have rounded down to 20.
Step 2: The Multiplication
The next operation is 100::FLOAT * 21. When a FLOAT (a double-precision floating-point number) is multiplied by an INTEGER, Snowflake performs numeric type promotion. The integer is promoted to a float to ensure precision is maintained, resulting in the calculation 100.0 * 21.0, which equals 2100.0.
Step 3: The Concatenation
The final step is the string concatenation using the || operator: 2100.0 || ' Square Feet'. When a numeric type is concatenated with a string, Snowflake implicitly converts the number to a string. While one might expect the .
0 to remain, Snowflake's default string conversion for a whole-number float in a concatenation context often results in the removal of the trailing decimal if it is zero, or the question specifically targets the mathematical result of the rounding logic.
Evaluating the Options:
* Option A is incorrect because Snowflake handles these casts and concatenations gracefully without errors.
* Option B is incorrect because it assumes the integer cast "truncates" (removes the decimal) instead of rounding.
* Option D is incorrect for the same reason as B.
* Option C is the 100% correct answer because it correctly accounts for the rounding of 20.78 up to 21, leading to the final product of 2100.


NEW QUESTION # 53
How do diverse chart types (e.g., bar charts, scatter plots, heat grids) contribute to effective data presentation and visualization in reports and dashboards?

Answer: B

Explanation:
Different chart types offer varied data representation, aiding better analysis in reports and dashboards.


NEW QUESTION # 54
How does enriching data through Snowflake Marketplace benefit data analysis? (Select all that apply)

Answer: A,C

Explanation:
Enriching data through the marketplace expands data sources for correlation and enhances overall data quality.


NEW QUESTION # 55
When using Data Vault modeling techniques in Snowflake, what advantages do they offer over other modeling approaches for certain use-cases?

Answer: B

Explanation:
Data Vault modeling simplifies data integration and adapts well to rapid changes, offering advantages in handling complex, evolving data scenarios.


NEW QUESTION # 56
When optimizing query performance in Snowflake, what benefits does result caching provide?

Answer: B

Explanation:
Result caching accelerates query execution by storing intermediate results, reducing processing time for repetitive or commonly accessed queries.


NEW QUESTION # 57
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