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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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 2: Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Time-series analysis
  • 2. Snowflake-specific analytical features
  • 3. Aggregate functions
Topic 3: Data Transformation and Data Modeling22%-30%- Design data models
  • 1. Data vault models
  • 2. Star schema design
  • 3. Snowflake schema design
- Transform data using SQL
  • 1. PIVOT/UNPIVOT operations
  • 2. Window functions
  • 3. QUALIFY clauses
  • 4. Common Table Expressions (CTEs)
Topic 4: Data Ingestion and Data Preparation15%-20%- 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
- Perform data discovery to identify what is needed from available datasets
  • 1. Query tables to assess data elements and statistics maintained by Snowflake
  • 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. 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
- Use a collection system to retrieve data
  • 1. Synthetic Data Generation
  • 2. Retrieve data from structured sources (CSV)
  • 3. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
  • 4. Retrieve data from unstructured sources
- 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)
- Implement data processing solutions
  • 1. Respond to processing failures
  • 2. Use logging and monitoring solutions (auditing, data lineage)
  • 3. Cleanse, conform, and enrich data
  • 4. Automate and implement data pipelines (scheduling)

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
When handling CSV, JSON, and Parquet data types for consumption, what advantages do Parquet files typically offer over the others?

Answer: D

Explanation:
Parquet files often provide better compression and query performance compared to CSV and JSON due to their columnar storage format, enhancing efficiency in handling large datasets.


NEW QUESTION # 28
Consider a table 'sales data' with columns 'product id', 'sale date', and 'revenue'. You need to calculate the cumulative revenue for each product over time, but only for the top 10 products by total revenue. What is the most efficient way to achieve this in Snowflake?

Answer: A

Explanation:
Option B is the most efficient. and 'QUALIFY allow filtering and ranking within a single query, and efficiently calculates the cumulative revenue. Option A requires a join which can be less performant. Option C involves creating a temporary table which adds overhead. Option D calculates cumulative revenue for all products before filtering, which is unnecessary work. Option E could be considered, however APPROX TOP_N is approximate and the requirement asks for an exact calculation.


NEW QUESTION # 29
How does leveraging partition pruning optimize query performance in Snowflake?

Answer: D

Explanation:
Partition pruning in Snowflake filters unnecessary partitions during query execution, enhancing query performance by minimizing the amount of data scanned.


NEW QUESTION # 30
A Data Analyst runs this query:

The Analyst men runs this query:

What will be the output?

Answer: B

Explanation:
Understanding how Snowflake aggregate functions like MIN() and MAX() handle numerical data and NULL values is fundamental for accurate Data Analysis. In this scenario, we have a table with five records distributed across two departments.
The MIN() function returns the smallest non-null value in the specified column across all rows in the group (or the entire table, if no GROUP BY is present). Looking at the salary column in the employees table, the values are: 10000, 9000, 8000, 15000, and NULL. The NULL value is ignored by the calculation. Among the remaining numerical values, 8000 is the smallest. Therefore, MIN_VAL will be 8000.
The MAX() function operates similarly, returning the largest non-null value from the set. Comparing the same list of numerical values-10000, 9000, 8000, and 15000-the largest value is clearly 15000. Consequently, MAX_VAL will be 15000.
Evaluating the Options based on the exhibit (image_8ca0df.png):
* Option A incorrectly identifies the minimum and maximum based on the employee_id column (where
2000 is max and 900 is min), rather than the requested salary column.
* Option B is incorrect as it seems to mix values from different columns or specific rows.
* Option C is incorrect because it mistakenly suggests that MIN() returns NULL if a NULL value is present in the column. In Snowflake, standard aggregate functions (except for specialized ones like ARRAY_AGG or certain window functions with specific clauses) skip NULL values entirely.
* Option D is the 100% correct output. It displays MIN_VAL as 8000 and MAX_VAL as 15000, which accurately reflects the mathematical minimum and maximum of the non-null entries in the salary column.
This behavior is consistent across all relational databases adhering to ANSI SQL standards, which Snowflake follows for these core aggregate operations.


NEW QUESTION # 31
Which of the following is a key step in data preparation?

Answer: B


NEW QUESTION # 32
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