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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Presentation and Data Visualization	28%-29%	- Integrate with BI tools 1. Other partner visualization tools 2. Power BI integration 3. Tableau integration - Create dashboards 1. Snowsight dashboards 2. Present analytical results
Topic 2: Data Ingestion and Data Preparation	15%-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. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 3. Determine the level of data granularity required 4. Evaluate required transformations (table joins, set operations, ASOF JOINS) 5. Identify elements required for business goals using BI reports or SQL analysis - Use a collection system to retrieve data 1. Synthetic Data Generation 2. Retrieve data from structured sources (CSV) 3. Retrieve data from unstructured sources 4. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) - Prepare data and load into Snowflake 1. Load files using Snowsight 2. Load data from external/internal stages into a table - Implement data processing solutions 1. Use logging and monitoring solutions (auditing, data lineage) 2. Respond to processing failures 3. Cleanse, conform, and enrich data 4. Automate and implement data pipelines (scheduling) - Enrich data by identifying and accessing relevant data from the Snowflake Marketplace 1. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) 2. Create tables and views 3. Find external data sets that correlate with available data - 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
Topic 3: Data Transformation and Data Modeling	22%-30%	- Transform data using SQL 1. QUALIFY clauses 2. PIVOT/UNPIVOT operations 3. Window functions 4. Common Table Expressions (CTEs) - Design data models 1. Star schema design 2. Data vault models 3. Snowflake schema design
Topic 4: Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Time-series analysis 2. Snowflake-specific analytical features 3. Aggregate functions

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q59-Q64):

NEW QUESTION # 59
When dealing with semi-structured data in Snowflake, what advantages do native data types offer over traditional relational data types?

A. Native data types offer higher data integrity and consistency
B. Native data types provide flexibility in handling varied data structures
C. Native data types enforce strict schema requirements
D. Native data types limit query complexity and optimization

Answer: B

Explanation:
Native data types in Snowflake offer flexibility in handling varied structures of semi-structured data, allowing for a more adaptable approach compared to traditional relational data types.

NEW QUESTION # 60
You are tasked with analyzing website clickstream data stored in a Snowflake table called 'clickstream_events'. Each row represents a click event and contains a 'session_id' , and a 'properties' column of type VARIANT that stores key-value pairs related to the event (e.g., '(page': '[product/123', 'element': You need to extract the 'page' and 'element' values from the 'properties' column and identify the most common 'page'-'element' combinations for each 'session_id'. Furthermore you need to limit the results of your data to the top 5 pages element pair. How can this task be accomplished using Snowflake table functions and analytical functions?

A. First create a view that flattens the JSON column using LATERAL FLATTEN, then select from this view to perform the group by and ranking operations.
B. Use multiple LATERAL FLATTEN calls, one for 'page' and one for 'element', then JOIN the results on and use QUALIFY ROW_NUMBER() OVER (PARTITION BY 'session_id' ORDER BY COUNT( ) DESC) 5.
C. Extract 'page' and 'element' using 'properties:page' and 'properties:element' directly in the SELECT statement, then use GROUP BY 'session_id', 'page', 'element' and QUALIFY ROW NUMBER() OVER (PARTITION BY 'session_id' ORDER BY COUNT( ) DESC) 5.
D. Create a UDF to parse the 'properties' VARIANT and return a table with 'page' and 'element columns, then JOIN this UDF's output with the original table and use QUALIFY ROW NUMBER() OVER (PARTITION BY 'session_id' ORDER BY COUNT( ) DESC) 5.
E. Use LATERAL FLATTEN to extract the keys and values from the 'properties' column, then use GROUP BY 'session_id', 'key', 'value' and COUNT( ) to find the most frequent combinations.

Answer: C

Explanation:
Option C is the most efficient and Snowflake-idiomatic way to achieve this. Directly accessing 'properties:page' and properties:element' is more performant than using LATERAL FLATTEN when you know the specific keys you need. The QUALIFY clause, combined with ROW NUMBER(), efficiently filters the results to the top 5 combinations per session. LATERAL FLATTEN is generally used when you need to iterate over an array within the VARIANT, not when you're extracting specific key-value pairs. UDF introduces extra overhead.

NEW QUESTION # 61
Which of the following statements regarding Secure Views and Materialized Views in Snowflake are CORRECT? (Choose two)

A. Secure Views can be used in conjunction with Row Access Policies to control data visibility at a granular level. Materialized views cannot be used in conjunction with row access policies.
B. Materialized Views can only be created on tables, not on other views or functions.
C. Secure Views obfuscate the underlying query logic from users, while Materialized Views store pre-computed data for faster query performance.
D. Secure Views incur a performance overhead compared to standard views, while Materialized Views generally improve query performance for suitable workloads.
E. Both Secure Views and Materialized Views can be automatically refreshed based on data changes in the underlying tables.

Answer: C,D

Explanation:
Secure Views hide query logic and data lineage, improving security. Materialized Views pre-compute and store data, improving performance. Secure Views can have a performance overhead as additional checks are performed. Materialized Views are automatically refreshed in Snowflake. Materialized views cannot be created with User-Defined Functions in the Select List or Where Clause. Row access policies are not supported on materialized views.

NEW QUESTION # 62
When manipulating data in Snowflake, what distinguishes aggregate functions from analytic functions?

A. Analytic functions return single calculated values
B. Aggregate functions handle distinct value sets only
C. Analytic functions operate on individual rows within a partition
D. Aggregate functions work on entire datasets

Answer: C

Explanation:
Analytic functions perform calculations on individual rows within a partition, while aggregate functions operate on entire datasets, making them distinct in their functionality.

NEW QUESTION # 63
How does enriching data through Snowflake Marketplace benefit data analysis? (Select all that apply)

A. Facilitates better data quality
B. Supports better data normalization
C. Increases data redundancy
D. Expands data sources for correlation

Answer: A,D

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

NEW QUESTION # 64
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