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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Ingestion and Data Preparation15%-20%- 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
- Prepare data and load into Snowflake
  • 1. Load files using Snowsight
  • 2. Load data from external/internal stages into a table
- Enrich data by identifying and accessing relevant data from the Snowflake Marketplace
  • 1. Find external data sets that correlate with available data
  • 2. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings)
  • 3. Create tables and views
- 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. Cleanse, conform, and enrich data
  • 2. Respond to processing failures
  • 3. Use logging and monitoring solutions (auditing, data lineage)
  • 4. Automate and implement data pipelines (scheduling)
- 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
Topic 2: Data Presentation and Data Visualization28%-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
Topic 3: Data Transformation and Data Modeling22%-30%- Transform data using SQL
  • 1. QUALIFY clauses
  • 2. PIVOT/UNPIVOT operations
  • 3. Common Table Expressions (CTEs)
  • 4. Window functions
- Design data models
  • 1. Star schema design
  • 2. Data vault models
  • 3. Snowflake schema design
Topic 4: Data Analysis30%-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 (Q17-Q22):

NEW QUESTION # 17
You are analyzing website traffic data in Snowflake using Snowsight. The data is stored in a table 'WEB TRAFFIC' with columns 'VISIT DATE (DATE), 'PAGE URL' (VARCHAR), 'VISITOR_ID (VARCHAR), and (INT - in seconds). You need to create a Snowsight dashboard to answer the following questions: 1. What is the average session duration per page URL? 2. What is the distribution of session durations (histogram)? 3. How many unique visitors are there per day? Which of the following approaches is the MOST efficient and appropriate for creating this Snowsight dashboard?

Answer: C

Explanation:
Option A is the most efficient and appropriate. Snowsight's direct SQL query functionality, coupled with the 'WIDTH_BUCKET' function, provides a straightforward and performant way to create the required visualizations. Using views (Option E) adds an unnecessary layer of abstraction. Trying to combine all visualizations into a single tile (Options B and D) is not suitable for the different visualization types. Using Snowsight Notebook (Option C) introduces unnecessary overhead for this relatively simple descriptive analysis task. While Notebooks are powerful, they're best used for more complex data transformations and analysis that SQL cannot easily handle.


NEW QUESTION # 18
You have two tables in Snowflake: 'ORDERS and 'CUSTOMERS. The 'ORDERS' table contains information about customer orders, including 'ORDER ID', 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. The 'CUSTOMERS table contains information about customers, including 'CUSTOMER ID', 'CUSTOMER NAME' , and 'CUSTOMER ADDRESS'. You need to create a view that joins these two tables based on 'CUSTOMER ID and includes only orders placed in the last 30 days. You also want to ensure that the view leverages the primary key information defined on the 'CUSTOMERS' table (even though Snowflake doesn't enforce it) for potential query optimizations. Which of the following SQL statements is the MOST efficient and best practice approach, considering Snowflake's optimizer?

Answer: B

Explanation:
Using 'CREATE OR REPLACE SECURE VIEW' is the best practice. 'CREATE OR REPLACE' allows you to update the view definition if needed without dropping and recreating it, minimizing disruption. A 'SECURE VIEW' prevents users from seeing the underlying query logic or accessing the base tables directly, enhancing security. The explicit JOIN syntax is preferred over the older implicit join syntax (option B). Snowflake's optimizer will leverage defined primary key relationships to optimize the join, even though it doesn't enforce them. Explicitly referencing schema isn't needed unless dealing with ambiguous names across schemas. The WHERE clause correctly filters for orders in the last 30 days.


NEW QUESTION # 19
In Snowsight, why is it significant to create various chart types (e.g., bar charts, scatter plots, heat grids) for data visualization?

Answer: D

Explanation:
Diverse chart types offer varied data representation, facilitating better analysis in Snowsight.


NEW QUESTION # 20
What distinguishes exploratory ad-hoc analyses from routine data analysis?

Answer: D

Explanation:
Ad-hoc analyses explore patterns and anomalies beyond established routines.


NEW QUESTION # 21
In Snowflake, how does Time Travel feature assist in data retrieval and analysis?

Answer: D

Explanation:
The Time Travel feature allows querying data as of specific timestamps, enabling historical data retrieval and analysis at various points in time.


NEW QUESTION # 22
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