Snowflake DAA-C01 Dumps Full Questions - Exam Study Guide

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Snowflake DAA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Transformation and Data Modeling22%-30%- Design data models
  • 1. Snowflake schema design
  • 2. Data vault models
  • 3. Star schema design
- Transform data using SQL
  • 1. PIVOT/UNPIVOT operations
  • 2. Common Table Expressions (CTEs)
  • 3. Window functions
  • 4. QUALIFY clauses
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 Ingestion and Data Preparation15%-20%- Prepare data and load into Snowflake
  • 1. Load data from external/internal stages into a table
  • 2. Load files using Snowsight
- Use a collection system to retrieve data
  • 1. Synthetic Data Generation
  • 2. Retrieve data from structured sources (CSV)
  • 3. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML)
  • 4. Retrieve data from unstructured sources
- Perform data discovery to identify what is needed from available datasets
  • 1. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE)
  • 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. Evaluate required transformations (table joins, set operations, ASOF JOINS)
- Implement data processing solutions
  • 1. Cleanse, conform, and enrich data
  • 2. Respond to processing failures
  • 3. Use logging and monitoring solutions (auditing, data lineage)
  • 4. Automate and implement data pipelines (scheduling)
- Use best practice considerations relating to data integrity structures
  • 1. Define primary keys for tables
  • 2. Implement constraints
  • 3. Perform table joins between parent/child tables
- 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
Topic 4: Data Presentation and Data Visualization28%-29%- Create dashboards
  • 1. Snowsight dashboards
  • 2. Present analytical results
- Integrate with BI tools
  • 1. Power BI integration
  • 2. Tableau integration
  • 3. Other partner visualization tools

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

NEW QUESTION # 18
You have developed a Snowsight dashboard for your marketing team that contains sensitive customer data'. You need to share this dashboard with a specific group of users, but ensure that they can only view the data and cannot modify the dashboard itself or the underlying queries. Which of the following steps should you take to securely share the dashboard?

Answer: E

Explanation:
Sharing with 'Can view' (B) ensures the users cannot modify the dashboard itself. Granting 'SELECT on the underlying tables/views and USAGE' on the database/schema allows them to see the data when the dashboard executes queries on their behalf. 'Can edit' (A) would allow them to modify the dashboard, which is not desired. 'MONITOR (C) is for monitoring warehouse usage, not data access. Exporting as PDF (D) is a workaround, but it loses interactivity and isn't a direct sharing method. E isn't correct because permissions on the data sources must be granted separately.


NEW QUESTION # 19
What effect do row access policies have on the creation of dashboards concerning user data visibility?

Answer: D

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


NEW QUESTION # 20
How do row access policies and Dynamic Data Masking impact the creation of dashboards concerning data visibility and security?

Answer: A

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


NEW QUESTION # 21
How can User-Defined Functions (UDFs) be utilized in SQL for data analysis?

Answer: B

Explanation:
UDFs expand SQL capabilities by enabling custom operations on data, extending beyond standard SQL functionalities.


NEW QUESTION # 22
You are analyzing sales data in Snowflake to identify seasonal trends and patterns. You have a table 'SALES DATA with columns 'SALE DATE (DATE) and 'SALE_AMOUNT (NUMBER). Which of the following SQL queries and visualization techniques would be MOST effective in identifying and visualizing these seasonal trends? Assume the data spans several years.

Answer: C

Explanation:
Option C is the most effective because it combines weekly sales aggregation with a box plot analysis of monthly sales across multiple years. The weekly aggregation provides a granular view of sales trends, while the box plot effectively visualizes the distribution of sales for each month, allowing for easy identification of monthly seasonal patterns and outliers. Option A only shows monthly sales volume, not the distribution of sales within each month across years. Option B shows the yearly trend, not seasonal variations. Option D doesn't aggregate the data and hence can't show you the seasonality. Option E only shows the daily variance across weeks.


NEW QUESTION # 23
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