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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q44-Q49):

NEW QUESTION # 44
You are preparing data for a machine learning model that predicts customer churn. You have a table 'CUSTOMER TRANSACTIONS with columns 'CUSTOMER ID', 'TRANSACTION DATE, and 'TRANSACTION AMOUNT'. Your model requires a feature representing the recency of the customer's last transaction (days since last transaction) calculated as of today. Which of the following SQL snippets in Snowflake best achieves this, considering potential performance implications on a large dataset?

Answer: A

Explanation:
The best approach is E because it explicitly casts 'CURRENT _ to a DATE data type using 'CAST(CURRENT This ensures consistency and avoids potential issues if the TRANSACTION DATE' column is also of DATE data type. provides a date only, while provides date and time. Using 'CURRENT directly in 'DATEDIFF with a DATE field could lead to unexpected results due to the time component. Also other options are incorrect because 'GETDATE()' does not exist in snowflake, and because is redundant since CURRENT TIMESTAMP returns a timestamp already


NEW QUESTION # 45
Which Snowflake feature allows users to encapsulate a series of SQL statements into a reusable database object, facilitating modular code development?

Answer: B


NEW QUESTION # 46
When performing a diagnostic analysis, what action aids in identifying demographics and relationships? (Select all that apply)

Answer: C,D

Explanation:
Analyzing statistical trends and collecting related data are crucial in identifying demographics and relationships in diagnostic analysis.


NEW QUESTION # 47
You are working on a data ingestion pipeline that loads data from a CSV file into a Snowflake table called The CSV file occasionally contains invalid characters in the 'Email' column (e.g., spaces, non-ASCII characters). You want to ensure data integrity and prevent the entire load from failing due to these errors. Which of the following strategies, used in conjunction, would BEST handle this situation during the COPY INTO command and maintain data quality?

Answer: A,B

Explanation:
Options B and E provide the most robust solution. ERROR = 'CONTINUE'' allows the load to proceed despite errors. Creating an error queue (implicitly handled by Snowflake if using allows you to examine and address the problematic records later. By including in the file format definition = TRUE' and 'ENCODING = 'UTF8" and 'VALIDATE' function during the 'COPY INTO command to identify erroneous Email columns, you can standardize character encoding. 'SKIP_FILE (options A, C, and D) might lose valuable data. While correcting data with SQL after the load (option C) is possible, capturing the error data directly during the load is more efficient.


NEW QUESTION # 48
What considerations are crucial when performing table joins between parent/child tables for maintaining data integrity? (Select all that apply)

Answer: A,B,C

Explanation:
Table joins require considerations such as matching data types, establishing foreign key constraints, and ensuring referential integrity for data integrity maintenance.


NEW QUESTION # 49
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