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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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 2: 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
- 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)
- 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. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
  • 2. Determine the level of data granularity required
  • 3. Query tables to assess data elements and statistics maintained by Snowflake
  • 4. Evaluate required transformations (table joins, set operations, ASOF JOINS)
  • 5. Identify elements required for business goals using BI reports or SQL analysis
- 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
Topic 3: Data Analysis30%-32%- Perform advanced analytics using SQL
  • 1. Time-series analysis
  • 2. Snowflake-specific analytical features
  • 3. Aggregate functions
Topic 4: Data Presentation and Data Visualization28%-29%- Integrate with BI tools
  • 1. Tableau integration
  • 2. Power BI integration
  • 3. Other partner visualization tools
- Create dashboards
  • 1. Snowsight dashboards
  • 2. Present analytical results

>> Practical DAA-C01 Information <<

DAA-C01 – 100% Free Practical Information | Efficient SnowPro Advanced: Data Analyst Certification Exam Free Braindumps

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q57-Q62):

NEW QUESTION # 57
What functions should a Data Analyst use to run descriptive analytics on a data set? (Select TWO).

Answer: B,E

Explanation:
Descriptive analytics is the process of using historical data to understand "what happened." This typically involves summarizing large datasets into interpretable chunks using central tendency, dispersion, and frequency measures.
AVG (Average) is a cornerstone of descriptive statistics. It provides the arithmetic mean of a numeric column, allowing an analyst to understand the "typical" value within a dataset (e.g., Average Order Value).
APPROX_COUNT_DISTINCT is a descriptive tool used to understand the volume of unique entities within a dataset (e.g., "How many unique customers visited the site?"). Similar to HLL mentioned earlier, this function provides a fast summary of data volume and variety, which is a primary goal of the descriptive phase of analysis.
Evaluating the Options:
* Options A and B (REGR_INTERCEPT and REGR_SLOPE) are used for linear regression. These fall under Predictive Analytics, as they are used to model relationships and predict future outcomes, rather than just describing current data.
* Option C (ROW_NUMBER) is a window function used for data ranking and ordering, but it does not provide a descriptive summary of the dataset's characteristics.
* Options D and E are correct because they provide summary statistics (mean and cardinality) that define the "state" of the data, which is the definition of descriptive analytics.


NEW QUESTION # 58
Configuring subscriptions and updates in a dashboard tool primarily helps in:

Answer: C


NEW QUESTION # 59
How do row access policies and Dynamic Data Masking impact the creation of dashboards in terms of data visibility and security?

Answer: B

Explanation:
Row access policies restrict data visibility based on user privileges, ensuring better security in dashboard creation.


NEW QUESTION # 60
You are working with a Snowflake table 'ORDERS that contains order data in a VARIANT column named 'ORDER DETAILS'. The 'ORDER DETAILS column contains JSON objects with nested arrays of product information, including 'product_id', 'quantity', and 'price'. You need to calculate the total revenue for each order. Which of the following SQL snippets correctly calculates the total revenue for each order using LATERAL FLATTEN and aggregation?

Answer: A,C

Explanation:
Snowflake requires explicit casting to numeric datatypes when performing arithmetic operations on VARIANT data. Options A and B do not cast the 'quantity' and 'price' fields to numbers, which would result in incorrect calculations. Option E uses a deprecated ' TO_NUMBER function.


NEW QUESTION # 61
What effect do row access policies have on the creation of dashboards concerning user data visibility?

Answer: A

Explanation:
Row access policies restrict data visibility based on user privileges, ensuring better security in dashboard creation.


NEW QUESTION # 62
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