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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Ingestion and Data Preparation15%-20%- Enrich data by identifying and accessing relevant data from the Snowflake Marketplace
  • 1. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings)
  • 2. Find external data sets that correlate with available data
  • 3. Create tables and views
- 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. Identify elements required for business goals using BI reports or SQL analysis
  • 2. Determine the level of data granularity required
  • 3. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
  • 4. Query tables to assess data elements and statistics maintained by Snowflake
  • 5. Evaluate required transformations (table joins, set operations, ASOF JOINS)
- Implement data processing solutions
  • 1. Respond to processing failures
  • 2. Automate and implement data pipelines (scheduling)
  • 3. Cleanse, conform, and enrich data
  • 4. Use logging and monitoring solutions (auditing, data lineage)
- 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. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
  • 2. Retrieve data from structured sources (CSV)
  • 3. Retrieve data from unstructured sources
  • 4. Synthetic Data Generation
Topic 2: 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. PIVOT/UNPIVOT operations
  • 2. Window functions
  • 3. Common Table Expressions (CTEs)
  • 4. QUALIFY clauses
Topic 3: Data Presentation and Data Visualization28%-29%- Create dashboards
  • 1. Present analytical results
  • 2. Snowsight dashboards
- Integrate with BI tools
  • 1. Other partner visualization tools
  • 2. Power BI integration
  • 3. Tableau integration
Topic 4: Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Aggregate functions
  • 2. Snowflake-specific analytical features
  • 3. Time-series analysis

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
A logistics company needs to determine which warehouses are within a 50km radius of a new distribution center. The warehouse locations are stored in a table 'WAREHOUSES' with columns 'WAREHOUSE ID' ONT), (GEOGRAPHY) and the distribution center's location is stored in a variable of type GEOGRAPHY. Which query will efficiently identify all warehouses within the specified radius, returning the 'WAREHOUSE ID and distance in kilometers?

Answer: B

Explanation:
The correct answer uses 'ST DWITHIN' with the correct parameters and unit. 'ST DWITHIN(LOCATION, @distribution_center, 50000)' correctly filters the warehouses based on the 50km (50000 meters) radius. ST_DISTANCE calculates the distance in meters, which is then converted to kilometers by dividing by 1000. The warehouse location should come first followed by the distribution centre in the DWITHIN' Function.


NEW QUESTION # 24
What does the "SQL keyword" refer to in the context of adding filters to a worksheet?

Answer: B

Explanation:
In Snowsight (Snowflake's web interface), Data Analysts can create interactive dashboards and worksheets using Filters. When you define a filter (such as a Date Range or a list of Regions), you must assign it a SQL Keyword.
This "SQL Keyword" acts as a variable placeholder within your SQL code. For example, if you create a filter for "Customer Region" and set the SQL keyword to :my_region, you can then write a query like:
SELECT * FROM sales WHERE region = :my_region. When a user interacts with the UI and selects "North America" from the dropdown, Snowsight automatically injects "North America" into every instance where :
my_region appears in the worksheet's SQL.
Evaluating the Options:
* Option A is incorrect because the keyword is a label/variable, not the underlying function code.
* Option C and Option D are incorrect as they confuse the data source of the filter values with the reference name used in the SQL code.
* Option B is the correct answer. The SQL Keyword is specifically the identifier (prefixed with a colon in the code) that allows the analyst to link the UI element (the filter) to the execution logic of the query.
This is a fundamental skill for the Data Presentation and Data Visualization domain, ensuring reports are dynamic and user-friendly.


NEW QUESTION # 25
In Snowflake, how does leveraging Parquet format contribute to optimizing query performance and storage efficiency?

Answer: D

Explanation:
Parquet format optimizes query performance and storage efficiency by enhancing data compression, reducing storage needs, and accelerating query execution in Snowflake.


NEW QUESTION # 26
When implementing data processing solutions, how does aggregating and enriching data contribute to the overall data analysis process?

Answer: A

Explanation:
Aggregating and enriching data enhances data granularity by providing more detailed and comprehensive insights into datasets, enabling better analysis and correlation of information.


NEW QUESTION # 27
What challenges might arise when handling JSON data in Snowflake using built-in functions for traversing, flattening, and nesting? (Select all that apply)

Answer: A,B

Explanation:
Challenges include handling nested structures and complexities in parsing and querying nested JSON objects, which might arise when manipulating JSON data in Snowflake.


NEW QUESTION # 28
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