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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Perform table joins between parent/child tables
  • 2. Implement constraints
  • 3. Define primary keys for tables
- Use a collection system to retrieve data
  • 1. Retrieve data from unstructured sources
  • 2. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
  • 3. Retrieve data from structured sources (CSV)
  • 4. Synthetic Data Generation
- Implement data processing solutions
  • 1. Cleanse, conform, and enrich data
  • 2. Respond to processing failures
  • 3. Automate and implement data pipelines (scheduling)
  • 4. Use logging and monitoring solutions (auditing, data lineage)
- Perform data discovery to identify what is needed from available datasets
  • 1. Query tables to assess data elements and statistics maintained by Snowflake
  • 2. Evaluate required transformations (table joins, set operations, ASOF JOINS)
  • 3. Identify elements required for business goals using BI reports or SQL analysis
  • 4. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
  • 5. Determine the level of data granularity required
- 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)
Topic 2: Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Time-series analysis
  • 2. Aggregate functions
  • 3. Snowflake-specific analytical features
Topic 3: Data Transformation and Data Modeling22%-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. Snowflake schema design
  • 3. Data vault models
Topic 4: 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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
A Data Analyst created a model called modelX using SNOWFLAKE.ML.FORECAST. The Analyst needs to predict the next few values and save the result directly into tableX. What step does the Analyst need to take after calling the modelX!FORECAST function?

Answer: D

Explanation:
Snowflake Cortex ML functions, such as FORECAST, return a tabular result set when called using the instance method syntax (e.g., CALL modelX!FORECAST(...)). While this output is visible in the Snowsight results pane, the CALL statement itself cannot be used directly as a subquery within a standard INSERT INTO or CREATE TABLE AS SELECT (CTAS) statement.
To persist the results of a model's prediction into a permanent table (tableX), the Data Analyst must utilize the RESULT_SCAN table function. Snowflake stores the results of every query and function call in a temporary cache for 24 hours. The RESULT_SCAN function allows you to treat that cache as a queryable table.
The standard workflow is:
* Execute the forecast: CALL modelX!FORECAST(FORECASTING_PERIODS => 12);
* Immediately after, use the LAST_QUERY_ID() function to identify the query that generated the forecast results.
* Create the table by querying that result set: CREATE TABLE tableX AS SELECT * FROM TABLE (RESULT_SCAN(LAST_QUERY_ID())); Evaluating the Options:
* Option A is incorrect because the CALL syntax does not support a direct INTO clause for table creation.
* Option B is incorrect because passing a table as an argument is part of the training or input phase, not the output persistence phase.
* Option D is overly complex and contains non-standard terminology ("List the cache content").
* Option C is the 100% correct answer. It reflects the required "post-processing" step in the Snowflake Data Cloud to bridge the gap between procedural model calls and relational table storage.


NEW QUESTION # 13
What actions can be taken when responding to processing failures in data processing solutions?
(Select all that apply)

Answer: A,B,D

Explanation:
Responding to processing failures involves logging error details, identifying the root cause, and resuming processing from the failure point.


NEW QUESTION # 14
What is the primary benefit of using secure views in data analysis?

Answer: C

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


NEW QUESTION # 15
You've identified a 'Filter' operation in a Snowflake query execution plan that is consuming a significant amount of time. The filter predicate involves a UDF (User-Defined Function) called 'calculate_score(columnl, column2)'. The UDF is written in Python. Analyzing the plan, you observe a high number of rows being processed by this filter. How can you optimize this scenario for faster query execution?

Answer: B,C

Explanation:
Options A and C provide significant performance improvements. A moves the computation to Snowflake's engine, likely improving speed. C pre-calculates the score, avoiding repeated UDF calls. While increasing warehouse size (B) might help, it doesn't address the fundamental inefficiency of the UDF. Caching within the UDF (E) can improve performance if there are repeated calls with the same inputs, but a SQL based code or materialized view is better solution in this case. Regular Expressions don't have computational power for any complex calculations.


NEW QUESTION # 16
A retail company has data about their products, sales, and inventory. They need a dashboard to visualize key metrics, including total sales, average order value, inventory levels, and product performance across different regions. The data is stored in the following tables: 'PRODUCTS (PRODUCT ID, PRODUCT NAME, CATEGORY, PRICE) 'SALES' (SALE_ID, PRODUCT_ID, SALE_DATE, QUANTITY, REGION) 'INVENTORY (PRODUCT ID, REGION, QUANTITY ON_HAND) Which of the following strategies will result in an efficient dashboard that allows users to quickly filter and drill down into the data by region, product category, and time period while minimizing query execution time? (Select all that apply.)

Answer: B,E

Explanation:
Search optimization (C) can significantly speed up queries on large tables by creating a search index on frequently used filter columns. Materialized views (D) are also beneficial because they pre-aggregate the data, reducing the amount of computation required at query time. Creating a single, wide denormalized table (A) can lead to data redundancy and increased storage costs. Joining separate views in the dashboard tool (B) can be inefficient, as the joins are performed at query time. Data masking policies (E) are important for security but don't directly optimize query performance for dashboards.


NEW QUESTION # 17
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