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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Snowflake-specific analytical features
  • 2. Time-series analysis
  • 3. Aggregate functions
Data Presentation and Data Visualization28%-29%- Create dashboards
  • 1. Snowsight dashboards
  • 2. Present analytical results
- Integrate with BI tools
  • 1. Tableau integration
  • 2. Other partner visualization tools
  • 3. Power BI integration
Data Ingestion and Data Preparation15%-20%- Use a collection system to retrieve data
  • 1. Synthetic Data Generation
  • 2. Retrieve data from unstructured sources
  • 3. Retrieve data from structured sources (CSV)
  • 4. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
- Perform data discovery to identify what is needed from available datasets
  • 1. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
  • 2. Evaluate required transformations (table joins, set operations, ASOF JOINS)
  • 3. Determine the level of data granularity required
  • 4. Identify elements required for business goals using BI reports or SQL analysis
  • 5. Query tables to assess data elements and statistics maintained by Snowflake
- Enrich data by identifying and accessing relevant data from the Snowflake Marketplace
  • 1. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings)
  • 2. Create tables and views
  • 3. Find external data sets that correlate with available data
- Use best practice considerations relating to data integrity structures
  • 1. Define primary keys for tables
  • 2. Perform table joins between parent/child tables
  • 3. Implement constraints
- Implement data processing solutions
  • 1. Use logging and monitoring solutions (auditing, data lineage)
  • 2. Cleanse, conform, and enrich data
  • 3. Automate and implement data pipelines (scheduling)
  • 4. Respond to processing failures
- Prepare data and load into Snowflake
  • 1. Load files using Snowsight
  • 2. Load data from external/internal stages into a table
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. Data vault models
  • 2. Snowflake schema design
  • 3. Star schema design

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q66-Q71):

NEW QUESTION # 66
When utilizing materialized views, what benefit do they offer in terms of query performance and data retrieval?

Answer: B

Explanation:
Materialized views provide precomputed snapshots, enhancing query performance.


NEW QUESTION # 67
You're tasked with creating a Snowsight dashboard to monitor the performance of different ETL pipelines. The dashboard needs to display the average run time and the number of errors for each pipeline over the last 7 days. The data is stored in a table called 'ETL LOGS' with columns 'end_time', and 'error_flag' (boolean). You need to present this information in a way that users can easily compare the performance of different pipelines. Which of the following SQL queries, used as the basis for a Snowsight tile, would be MOST appropriate for this dashboard?

Answer: C

Explanation:
Option A is the most accurate. It correctly calculates the average run time in seconds using start_time, end_time)' and the error count using a conditional aggregation 'SUM(CASE WHEN error_flag THEN 1 ELSE 0 END)'. Option B is incorrect because - start_timey will not return runtime in seconds, it returns a fractional number of days. Option C uses IFF and datediff which is acceptable. Option D only counts the total logs and doesn't filter if there is an error. Option E is also technically correct by converting the boolean to a number to sum. Option A uses standard SQL which might be more preferable.


NEW QUESTION # 68
When working with semi-structured data in Snowflake, how do built-in functions for traversing, flattening, and nesting aid in data manipulation?

Answer: B

Explanation:
Built-in functions for semi-structured data in Snowflake simplify handling complex and nested structures, making data manipulation more manageable and enhancing flexibility in data transformation.


NEW QUESTION # 69
How does leveraging window functions in Snowflake differ from using table functions for data manipulation?

Answer: B

Explanation:
Window functions process data within specified partitions or frames, while table functions generate tables as their output, differing in their scope and operation.


NEW QUESTION # 70
When employing window functions versus table functions in Snowflake, how do they differ in their application and output?

Answer: C

Explanation:
Table functions in Snowflake process data within specified partitions or frames, whereas window functions generate aggregate results and operate on entire datasets, differing in their scope and operation.


NEW QUESTION # 71
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