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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. Synthetic Data Generation
  • 2. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
  • 3. Retrieve data from unstructured sources
  • 4. Retrieve data from structured sources (CSV)
- Perform data discovery to identify what is needed from available datasets
  • 1. Determine the level of data granularity required
  • 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. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
- Prepare data and load into Snowflake
  • 1. Load data from external/internal stages into a table
  • 2. Load files using Snowsight
- Implement data processing solutions
  • 1. Automate and implement data pipelines (scheduling)
  • 2. Cleanse, conform, and enrich data
  • 3. Respond to processing failures
  • 4. Use logging and monitoring solutions (auditing, data lineage)
- 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
- 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
Topic 2: Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Time-series analysis
  • 2. Snowflake-specific analytical features
  • 3. Aggregate functions
Topic 3: Data Transformation and Data Modeling22%-30%- Transform data using SQL
  • 1. PIVOT/UNPIVOT operations
  • 2. Window functions
  • 3. Common Table Expressions (CTEs)
  • 4. QUALIFY clauses
- Design data models
  • 1. Data vault models
  • 2. Star schema design
  • 3. Snowflake schema design
Topic 4: Data Presentation and Data Visualization28%-29%- Integrate with BI tools
  • 1. Tableau integration
  • 2. Other partner visualization tools
  • 3. Power BI integration
- Create dashboards
  • 1. Present analytical results
  • 2. Snowsight dashboards

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q18-Q23):

NEW QUESTION # 18
Consider a scenario where you are building a dashboard to monitor the performance of a marketing campaign. The data includes daily ad spend, website conversions, and cost per acquisition (CPA). The stakeholders need to quickly assess whether the campaign is meeting its target CPA. What visualization type would be MOST appropriate to display the current CPA compared to the target CPA, providing a clear and concise view of performance?

Answer: A

Explanation:
A gauge chart is specifically designed to display a single value (the current CPA) in relation to a target value (the target CPA). The color-coded zones provide an immediate indication of whether the campaign is performing well, needs improvement, or is failing. Line charts show trends, bar charts compare averages, and scatter plots show relationships. Pie chart showing CPA percentage against target CPA percentage do not accurately show CPA against target CPA, for better visualization gauge charts would be the preffered option


NEW QUESTION # 19
When performing a diagnostic analysis, what action aids in identifying demographics and relationships? (Select all that apply)

Answer: B,D

Explanation:
Analyzing statistical trends and collecting related data are crucial in identifying demographics and relationships in diagnostic analysis.


NEW QUESTION # 20
When implementing pre-math calculations (e.g., randomization, ranking, grouping) in Snowflake, how do they contribute to data analysis?

Answer: C

Explanation:
Pre-math calculations like ranking assist in summarizing and ordering data, enabling meaningful insights and comparisons in data analysis.


NEW QUESTION # 21
How do row access policies and Dynamic Data Masking affect the creation and maintenance of reports and dashboards?

Answer: A

Explanation:
Row access policies restrict data visibility based on user privileges, ensuring better security in creation and maintenance of reports and dashboards.


NEW QUESTION # 22
You are tasked with enriching a customer dataset in Snowflake. The 'CUSTOMER DATA table contains customer IDs and country codes. You have a separate 'COUNTRY INFORMATION' table that contains country codes and corresponding currency codes. Both tables reside in the 'RAW DATA schema of the 'ANALYTICS DB' database. You need to create a view called ENRICHED CUSTOMER DATA' in the 'TRANSFORMED DATA' schema that joins these tables to add currency information to the customer data'. You want to optimize this view for performance. Which of the following approaches would be the MOST efficient and scalable, considering potential data volume increases?

Answer: D

Explanation:
Materialized views generally provide better performance than standard views for complex queries, especially with joins. Clustering on the 'COUNTRY_CODE column further enhances performance by physically organizing the data based on this column, making lookups more efficient. Using secure view wouldn't impact on performance. Regular view refreshment using scheduled task is not as efficient compared to materialized view. UDFs can introduce performance overhead, especially for large datasets.


NEW QUESTION # 23
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