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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Ingestion and Data Preparation	15%-20%	- Prepare data and load into Snowflake 1. Load files using Snowsight 2. Load data from external/internal stages into a table - Perform data discovery to identify what is needed from available datasets 1. Evaluate required transformations (table joins, set operations, ASOF JOINS) 2. Identify elements required for business goals using BI reports or SQL analysis 3. Determine the level of data granularity required 4. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 5. Query tables to assess data elements and statistics maintained by Snowflake - Use a collection system to retrieve data 1. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 2. Retrieve data from unstructured sources 3. Retrieve data from structured sources (CSV) 4. Synthetic Data Generation - Implement data processing solutions 1. Respond to processing failures 2. Cleanse, conform, and enrich data 3. Automate and implement data pipelines (scheduling) 4. Use logging and monitoring solutions (auditing, data lineage) - 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) - Use best practice considerations relating to data integrity structures 1. Implement constraints 2. Define primary keys for tables 3. Perform table joins between parent/child tables
Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Time-series analysis 2. Aggregate functions 3. Snowflake-specific analytical features
Data Presentation and Data Visualization	28%-29%	- Create dashboards 1. Present analytical results 2. Snowsight dashboards - Integrate with BI tools 1. Tableau integration 2. Other partner visualization tools 3. Power BI integration
Data Transformation and Data Modeling	22%-30%	- Transform data using SQL 1. Common Table Expressions (CTEs) 2. PIVOT/UNPIVOT operations 3. Window functions 4. QUALIFY clauses - Design data models 1. Star schema design 2. Data vault models 3. Snowflake schema design

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q24-Q29):

NEW QUESTION # 24
What is the primary benefit of using secure views in data analysis?

A. Secure views offer enhanced data security while allowing selective data access.
B. Secure views simplify complex data structures more effectively than materialized views.
C. They don't impact data security but significantly enhance query performance.
D. They prevent the creation of materialized views.

Answer: A

Explanation:
Secure views enhance data security while allowing selective data access.

NEW QUESTION # 25
How does leveraging Time Travel feature in Snowflake aid in query optimization and historical data analysis?

A. Enables querying of historical data versions
B. Provides real-time data updates
C. Limits query execution time
D. Offers options to alter query execution plans

Answer: A

Explanation:
Time Travel feature allows querying historical data versions, facilitating historical data analysis and retrospective query optimizations based on past data states.

NEW QUESTION # 26
When connecting BI tools to Snowflake for dashboard creation, what factors need to be considered for seamless integration? (Select all that apply)

A. Data encryption requirements
B. Network latency between BI tool and Snowflake
C. Compatibility of BI tool with Snowflake
D. Availability of a Snowflake account only

Answer: A,B,C

Explanation:
Seamless integration requires considering compatibility, encryption, and network latency between BI tools and Snowflake.

NEW QUESTION # 27
A data analyst needs to ingest data from various sources into Snowflake, cleanse it, and load it into target tables. Which of the following actions are MOST crucial for ensuring data quality and consistency during the ingestion and preparation phases?

A. Storing all data in a single large table to avoid data silos.
B. Using Snowflake's data masking policies to protect sensitive data during ingestion.
C. Implementing data profiling techniques to identify data quality issues and inconsistencies before loading data into target tables and creating detailed data dictionaries.
D. Implementing data validation checks using Snowflake user-defined functions (UDFs) or stored procedures to detect and handle invalid data.
E. Enforcing data type constraints on Snowflake table columns.

Answer: C,D,E

Explanation:
Options A, B, and E are most crucial for data quality and consistency. Enforcing data type constraints (A) ensures that only data of the correct type is loaded into the tables. Implementing data validation checks (B) using UDFs or stored procedures allows for detecting and handling invalid data, preventing it from corrupting the data warehouse. Data profiling (E) helps to understand data quality issues before the load and guides data cleansing efforts. Storing all data in a single table (C) is generally not recommended as it can lead to performance issues and make it harder to manage data. While data masking (D) is important for data security, it is not directly related to data quality and consistency during ingestion and preparation. Data dictionaries promote data quality and usability.

NEW QUESTION # 28
When conducting diagnostic analysis, why is it essential to collect related data and demographics?

A. To restrict analysis to recent data only
B. To overlook statistical trends in historical data
C. To focus solely on isolated anomalies
D. It assists in identifying reasons/causes of anomalies in historical data

Answer: D

Explanation:
Collecting related data and demographics aids in identifying reasons/causes behind anomalies in historical data.

NEW QUESTION # 29
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