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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Snowflake-specific analytical features
  • 2. Aggregate functions
  • 3. Time-series analysis
Topic 2: Data Ingestion and Data Preparation15%-20%- Implement data processing solutions
  • 1. Respond to processing failures
  • 2. Cleanse, conform, and enrich data
  • 3. Use logging and monitoring solutions (auditing, data lineage)
  • 4. Automate and implement data pipelines (scheduling)
- Enrich data by identifying and accessing relevant data from the Snowflake Marketplace
  • 1. Find external data sets that correlate with available data
  • 2. Create tables and views
  • 3. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings)
- 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. Implement constraints
  • 2. Define primary keys for tables
  • 3. Perform table joins between parent/child tables
- Perform data discovery to identify what is needed from available datasets
  • 1. Identify elements required for business goals using BI reports or SQL analysis
  • 2. Evaluate required transformations (table joins, set operations, ASOF JOINS)
  • 3. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
  • 4. Query tables to assess data elements and statistics maintained by Snowflake
  • 5. Determine the level of data granularity required
- Use a collection system to retrieve data
  • 1. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
  • 2. Synthetic Data Generation
  • 3. Retrieve data from structured sources (CSV)
  • 4. Retrieve data from unstructured sources
Topic 3: Data Transformation and Data Modeling22%-30%- Transform data using SQL
  • 1. Window functions
  • 2. Common Table Expressions (CTEs)
  • 3. QUALIFY clauses
  • 4. PIVOT/UNPIVOT operations
- Design data models
  • 1. Star schema design
  • 2. Snowflake schema design
  • 3. Data vault models
Topic 4: Data Presentation and Data Visualization28%-29%- Create dashboards
  • 1. Snowsight dashboards
  • 2. Present analytical results
- Integrate with BI tools
  • 1. Other partner visualization tools
  • 2. Tableau integration
  • 3. Power BI integration

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q55-Q60):

NEW QUESTION # 55
What distinguishes exploratory ad-hoc analyses from routine data analysis?

Answer: B

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


NEW QUESTION # 56
You have a large dataset in Snowflake containing customer order information stored in a table named 'ORDERS' with columns 'ORDER_ID' ONT), 'CUSTOMER_ID' ONT), 'ORDER_DATE (DATE), 'TOTAL_AMOUNT' (FLOAT), and 'DISCOUNT_APPLIED' (BOOLEAN). You need to use Snowsight dashboards to analyze customer spending behavior and identify potential outliers. Which of the following visualizations, combined with appropriate SQL queries, would be MOST effective in identifying customers with unusually high or low order values? (Select TWO)

Answer: D,E

Explanation:
Options A and C are the most effective. A Box Plot (A) is ideal for identifying outliers in a distribution. By visualizing the distribution of total order amounts per customer, you can easily spot customers with unusually high or low spending. A Scatter Plot (C) directly shows the relationship between customer ID and total spending, making it easy to visually identify outliers based on their position relative to other data points. Option B is more suitable for trend analysis over time, and options D and E are useful but don't directly highlight individual customer outliers in terms of order value.


NEW QUESTION # 57
In data presentations for business use analyses, why is identifying patterns and trends crucial?

Answer: D

Explanation:
Identifying patterns and trends aids in insightful analyses in business use scenarios.


NEW QUESTION # 58
When creating reports and dashboards, how does evaluating data based on business requirements impact the visualization process?

Answer: A

Explanation:
Evaluating data based on business requirements ensures the dashboard contains relevant and useful content, improving its quality.


NEW QUESTION # 59
You are working with a table 'ORDERS' containing order data, and a table 'CUSTOMER SEGMENTS containing customer segment information. The 'ORDERS' table has columns 'ORDER ID', 'CUSTOMER ID, and 'ORDER AMOUNT'. The 'CUSTOMER SEGMENTS' table has columns 'CUSTOMER ID', 'SEGMENT ID', and 'SEGMENT NAME'. You need to create a query that enriches the 'ORDERS' table with the customer segment information. However, a customer can belong to multiple segments. You want to include all segments a customer belongs to in the enriched data, resulting in potentially multiple rows per order if the customer is in multiple segments. The output should include 'ORDER ID, 'ORDER AMOUNT, 'SEGMENT ID', and 'SEGMENT NAMES. Which SQL statement would correctly enrich the ORDERS table without losing any order information, even if customers belong to multiple segments?

Answer: E

Explanation:
An 'INNER JOIN' is the correct choice here. It will return all matching rows between the 'ORDERS' and tables based on the 'CUSTOMER ID. If a customer belongs to multiple segments, each segment will be returned in a separate row associated with the order. "LEFT JOIN' includes rows from the left table even without a match in the right table which is not needed. 'GROUP BY' in the INNER JOIN' will combine multiple segments with 'ORDER_IDS into one row, which is not needed as all segment names are expected. The subquery approach would only return one segment name per order. 'RIGHT JOIN' include all customer segments on the right which is also not needed.


NEW QUESTION # 60
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