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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Data Transformation and Data Modeling22%-30%- Transform data using SQL
  • 1. QUALIFY clauses
  • 2. PIVOT/UNPIVOT operations
  • 3. Window functions
  • 4. Common Table Expressions (CTEs)
- Design data models
  • 1. Snowflake schema design
  • 2. Data vault models
  • 3. Star schema design
Data Ingestion and Data Preparation15%-20%- Prepare data and load into Snowflake
  • 1. Load files using Snowsight
  • 2. Load data from external/internal stages into a table
- Use a collection system to retrieve data
  • 1. Synthetic Data Generation
  • 2. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
  • 3. Retrieve data from structured sources (CSV)
  • 4. Retrieve data from unstructured sources
- Enrich data by identifying and accessing relevant data from the Snowflake Marketplace
  • 1. Create tables and views
  • 2. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings)
  • 3. Find external data sets that correlate with available data
- 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
- 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. Identify elements required for business goals using BI reports or SQL analysis
  • 5. Evaluate required transformations (table joins, set operations, ASOF JOINS)
- Implement data processing solutions
  • 1. Use logging and monitoring solutions (auditing, data lineage)
  • 2. Respond to processing failures
  • 3. Automate and implement data pipelines (scheduling)
  • 4. Cleanse, conform, and enrich data
Data Presentation and Data Visualization28%-29%- Integrate with BI tools
  • 1. Other partner visualization tools
  • 2. Tableau integration
  • 3. Power BI integration
- Create dashboards
  • 1. Snowsight dashboards
  • 2. Present analytical results
Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Snowflake-specific analytical features
  • 2. Time-series analysis
  • 3. Aggregate functions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
Table TB_A with column COL_B contains an ARRAY. Which statement will select the last element of the ARRAY?

Answer: A

Explanation:
Working with semi-structured data types like Arrays is a core competency for a Snowflake Data Analyst. In Snowflake, arrays are zero-indexed, meaning the first element is at position 0. Consequently, the index of the last element is always the total number of elements minus one ($Size - 1$).
To retrieve an element from a specific index, Snowflake provides the GET() function. This function takes the array column and the calculated index as arguments. When combined with ARRAY_SIZE(), which returns the total count of elements in the array, the formula ARRAY_SIZE(COL_B)-1 accurately targets the final index regardless of the array's length.
Evaluating the Options:
* Option B is incorrect because using ARRAY_SIZE as the index directly (without subtracting 1) results in an "out-of-bounds" error or returns NULL, because the index equals the length (e.g., in an array of 3 items, the max index is 2).
* Option C is incorrect. While some programming languages (like Python) allow negative indexing to start from the end, Snowflake SQL does not support this shorthand for arrays; it would simply return NULL.
* Option D is incorrect because LAST_VALUE is an Analytic/Window function used to find the last row in a sorted result set, not the last element within a single array cell.
* Option A is the 100% correct approach. It uses the standard, robust method for dynamic index calculation. This ensures that even if different rows have arrays of different lengths, the query will always successfully "pluck" the final item from each. This skill is vital for Data Transformation tasks, such as extracting the most recent status from a history array.


NEW QUESTION # 20
What factors should be considered when evaluating which transformations are required in data discovery? (Select all that apply)

Answer: B,C,D

Explanation:
Evaluating necessary transformations involves considering data consistency, normalization, and alignment with business use cases.


NEW QUESTION # 21
How can automated and repeatable tasks contribute to maintaining reports and dashboards to meet business requirements?

Answer: B

Explanation:
Automated tasks ensure consistency and reduce manual effort in maintaining reports and dashboards.


NEW QUESTION # 22
When utilizing geospatial functions in Snowflake, what functionalities do these functions offer?
(Select all that apply)

Answer: A,B,D

Explanation:
Geospatial functions in Snowflake support geometric calculations, location-based analysis, and spatial indexing for geospatial data.


NEW QUESTION # 23
A retail company is using Snowflake to store their daily sales data'. They want to predict sales for the next week, accounting for weekly seasonality and a promotional campaign running on specific days. The sales data is in a table called 'SALES DATA' with columns 'SALE DATE' (DATE) and 'SALES AMOUNT' (NUMBER). Which of the following SQL statements is the MOST efficient and accurate way to achieve this using Snowflake's forecasting features, assuming a confidence interval of 95%?

Answer: B

Explanation:
Option E is the most correct. Option A uses 'timeLimit' in generator which needs additional conditions to be satisfied. Option E utilizes 'TIMESTAMP_INPUT = TRUE to correctly interpret the 'SALE_DATE column. It accurately specifies the forecast horizon (7 days) and confidence interval (0.95), and generates the future dates for forecasting using 'TABLE(GENERATOR(rowcount => 7))' to ensure the generation of 7 days for forecasting. Option D has OVER (ORDER BY NULL)' which is generally less performant than seq4().


NEW QUESTION # 24
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