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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q38-Q43):

NEW QUESTION # 38
When managing Snowsight dashboards, what role do subscriptions and updates play in meeting business requirements?

A. Subscriptions and updates don't impact dashboard management.
B. They enhance dashboard usability without impacting data updates.
C. Managing subscriptions and updates complicates dashboard usage.
D. Subscriptions and updates ensure timely information delivery.

Answer: D

Explanation:
Subscriptions and updates ensure timely information delivery, meeting business requirements.

NEW QUESTION # 39
When performing forecasting using statistics and built-in functions, what role do these functions play?

A. They restrict forecasting to predefined models without flexibility.
B. They solely aid in data visualization without impacting the forecasting process.
C. They enable the creation of custom forecasting models as needed.
D. These functions only apply to limited data types for forecasting.

Answer: C

Explanation:
Built-in functions in statistics facilitate the creation of custom forecasting models, allowing flexibility in the forecasting process.

NEW QUESTION # 40
You are responsible for maintaining a dashboard displaying real-time website traffic data'. The data is ingested into a Snowflake table named 'WEB EVENTS using Snowpipe from cloud storage. The 'WEB EVENTS' table includes 'EVENT TIMESTAMP' , 'PAGE URL' , and 'USER ID columns. The dashboard requires near real-time updates, but you are noticing significant latency. Which of the following actions, performed in isolation, is LEAST likely to improve the dashboard update frequency?

A. Reduce the frequency of micro-batch data loading by increasing the Snowpipe 'AUTO_INGEST schedule to reduce the number of pipe executions per minute.
B. Refactor dashboard queries to directly query the 'WEB EVENTS' table without any aggregations or transformations.
C. Create a materialized view that pre-aggregates the data needed for the dashboard and configure it for automatic refresh.
D. Increase the size of the Snowflake virtual warehouse used for Snowpipe data loading.
E. Optimize the Snowpipe configuration by adjusting the 'COPY INTO' statement to use file format options appropriate for the source data (e.g., compression, field delimiters).

Answer: A

Explanation:
Reducing the frequency of micro-batch data loading (option E) is LEAST likely to improve dashboard update frequency; in fact, it will decrease it. The dashboard needs near real-time updates, so reducing how often data is loaded will directly conflict with that requirement. All the other options are designed to help with optimization. Optimizing Snowpipe configuration, creating materialized views for pre-aggregation, and increasing warehouse size for Snowpipe can help, too. Reducing the amount of aggregations that dashboard needs will help since less processing is necessary.

NEW QUESTION # 41
You are analyzing a query profile and observe a high percentage of time spent in 'Remote Scan'. Which of the following actions would be MOST effective in reducing this bottleneck and improving query performance?

A. Increase the warehouse size to allow more concurrent operations.
B. Convert the table to a transient table to reduce metadata overhead.
C. Implement clustering on the table based on the filter criteria used in the query.
D. Create a materialized view that pre-computes the results of the query.
E. Add a 'LIMIT' clause to the query to reduce the amount of data scanned.

Answer: C

Explanation:
High 'Remote Scan' time indicates that Snowflake is spending a significant portion of its time fetching data from remote storage. Clustering on the table based on the query's filter criteria will help Snowflake quickly identify and retrieve only the relevant data, reducing the amount of data scanned remotely.

NEW QUESTION # 42
How does leveraging partition pruning optimize query performance in Snowflake?

A. Increases storage requirements for optimized query access
B. Reduces data accessibility across multiple warehouses
C. Filters unnecessary partitions during query execution
D. Limits query complexity and optimization possibilities

Answer: C

Explanation:
Partition pruning in Snowflake filters unnecessary partitions during query execution, enhancing query performance by minimizing the amount of data scanned.

NEW QUESTION # 43
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