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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Aggregate functions
  • 2. Snowflake-specific analytical features
  • 3. Time-series analysis
Topic 2: Data Transformation and Data Modeling22%-30%- Transform data using SQL
  • 1. Common Table Expressions (CTEs)
  • 2. PIVOT/UNPIVOT operations
  • 3. Window functions
  • 4. QUALIFY clauses
- Design data models
  • 1. Data vault models
  • 2. Snowflake schema design
  • 3. Star schema design
Topic 3: Data Presentation and Data Visualization28%-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 4: Data Ingestion and Data Preparation15%-20%- Prepare data and load into Snowflake
  • 1. Load files using Snowsight
  • 2. Load data from external/internal stages into a table
- 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
- Implement data processing solutions
  • 1. Automate and implement data pipelines (scheduling)
  • 2. Use logging and monitoring solutions (auditing, data lineage)
  • 3. Cleanse, conform, and enrich data
  • 4. Respond to processing failures
- Perform data discovery to identify what is needed from available datasets
  • 1. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
  • 2. Identify elements required for business goals using BI reports or SQL analysis
  • 3. Evaluate required transformations (table joins, set operations, ASOF JOINS)
  • 4. Query tables to assess data elements and statistics maintained by Snowflake
  • 5. Determine the level of data granularity required
- 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. Synthetic Data Generation
  • 4. Retrieve data from structured sources (CSV)
- 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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q49-Q54):

NEW QUESTION # 49
Why is the Parquet format preferred for complex data sets?

Answer: B


NEW QUESTION # 50
How do materialized views differ from regular views in terms of data storage and computation?

Answer: A

Explanation:
Materialized views provide precomputed snapshots, differentiating them from regular views.


NEW QUESTION # 51
A data analyst is experiencing slow query performance when joining two large tables, 'SALES' (1 billion rows) and 'CUSTOMERS' (10 million rows), on 'CUSTOMER ID. The 'SALES' table is frequently updated. The following query is used: SELECT s. , c. FROM SALES s JOIN CUSTOMERS c ON s.CUSTOMER_lD = c.CUSTOMER_lD WHERE s.SALE_DATE DATEADD(day, -30, CURRENT DATE()); Which of the following strategies would MOST effectively improve the query performance, assuming you have appropriate privileges to alter objects and cost is a concern?

Answer: A

Explanation:
Creating 'SALES LAST 30 DAYS' table and joining is the most effective strategy because it reduces the size of the SALES table significantly before the join operation. Materialized views (A) are generally effective, but the high update frequency of the SALES table could lead to significant overhead in materialized view maintenance. Standard indexes (B) are not supported in Snowflake. Search optimization (C) may not be suitable as the could have high cardinality. Clustering (D) could improve performance, but the benefits might not outweigh the cost of reclustering after frequent data loading, and it does not limit the initial size of the join.


NEW QUESTION # 52
A Snowflake table 'transactions' stores data about financial transactions. The table includes the following columns: 'transaction_id' (INTEGER), 'account_id' (INTEGER), 'transaction_date' (DATE), and 'transaction_amount' (NUMBER). You need to analyze the moving average of transaction amounts for each account over a 7-day window. The moving average should be calculated for each transaction date, considering the 3 preceding days, the current day, and the 3 following days. You want to show the 'account_id' , 'transaction_date', 'transaction amount', and the calculated 'moving_average". What's the most appropriate and efficient Snowflake query to perform this calculation?

Answer: C

Explanation:
Option B is the correct answer. The 'PARTITION BY account_id' clause ensures that the moving average is calculated separately for each account. The 'ORDER BY transaction_date ASCS clause orders the transactions within each account by date. The 'ROWS BETWEEN 3 PRECEDING AND 3 FOLLOWING' clause defines the window frame as the current row, the 3 preceding rows, and the 3 following rows based on row number (not date ranges). Option A doesn't partition by account_id, so the moving average will be across all accounts which is not what we want. Option C uses 'RANGE instead of which relies on finding all entries within that date range, that may have many entries making the numbers incorrect. Option D only considers preceding rows, not following ones. Option E doesn't have any windowing and therefore the data is calculated to the current point, without consideration for following or previous days.


NEW QUESTION # 53
How do stored procedures contribute to data analysis efficiency in SQL compared to UDFs?

Answer: D

Explanation:
Stored procedures enhance efficiency by enabling the execution of repetitive tasks.


NEW QUESTION # 54
......

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