100% Pass Quiz Snowflake - DAA-C01 Accurate Top Dumps

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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q53-Q58):

NEW QUESTION # 53
How do logging and monitoring solutions contribute to data processing solutions? (Select all that apply)

Answer: A,D

Explanation:
Logging and monitoring solutions help respond to processing failures promptly and provide insights into processing status.


NEW QUESTION # 54
A data analyst is tasked with creating a near-real-time dashboard using Streamlit and Snowflake to monitor website traffic. The website traffic data is continuously ingested into a Snowflake table named 'WEB TRAFFIC EVENTS' with columns 'EVENT TIME' (TIMESTAMP LTZ), 'PAGE URL' (VARCHAR), and 'USER ID' (VARCHAR). The analyst wants to ensure the Streamlit dashboard automatically reflects the latest data in Snowflake with minimal latency. Which of the following approaches would provide the MOST efficient and near-real-time data updates in the Streamlit dashboard?

Answer: B

Explanation:
Leveraging Snowflake's CDC capabilities is the most efficient way to achieve near-real-time data updates in Streamlit. CDC allows you to retrieve only the changes made to the table since the last refresh, significantly reducing the amount of data transferred and processed compared to querying the entire table each time. Snowpipe is a good option, but is more oriented to data ingestion and not data changes. Option A caches results, which is opposite of real time. Option B is useful, but more complex than needed. Option C is not related to the problem. Option D is incorrect because 'auto-commit' applies only to data updates from the application to Snowflake. Streamlit can then query this CDC stream to get an updated set of data. Using 'st.cache_data' with a short TTL ensures the dashboard reflects recent data changes from the CDC Stream.


NEW QUESTION # 55
A retail company is using Snowflake to store their daily sales data'. They want to predict sales for the next week, accounting for weekly seasonality and a promotional campaign running on specific days. The sales data is in a table called 'SALES DATA' with columns 'SALE DATE' (DATE) and 'SALES AMOUNT' (NUMBER). Which of the following SQL statements is the MOST efficient and accurate way to achieve this using Snowflake's forecasting features, assuming a confidence interval of 95%?

Answer: A

Explanation:
Option E is the most correct. Option A uses 'timeLimit' in generator which needs additional conditions to be satisfied. Option E utilizes 'TIMESTAMP_INPUT = TRUE to correctly interpret the 'SALE_DATE column. It accurately specifies the forecast horizon (7 days) and confidence interval (0.95), and generates the future dates for forecasting using 'TABLE(GENERATOR(rowcount => 7))' to ensure the generation of 7 days for forecasting. Option D has OVER (ORDER BY NULL)' which is generally less performant than seq4().


NEW QUESTION # 56
You are tasked with analyzing website traffic data stored in a Snowflake table named 'page_views'. The table has columns 'user_id' (INT), 'page_url (VARCHAR), and 'view_time' (TIMESTAMP N T Z). You need to identify users who are likely bots based on an unusually high number of page views within a short period. Specifically, you want to flag users who have more than 100 page views within any 5-minute window Which of the following queries is the MOST efficient and accurate way to achieve this?

Answer: E

Explanation:
Option C is the most accurate and efficient. It correctly calculates the number of views within a 5-minute window for each user using the 'DATE DIFF function and a window function. It then filters to only include users who exceed 100 views in any of those windows, and ensures each user is only counted once with the DISTINCT keyword. Option A is incorrect because it only looks at total page views across the entire dataset. Option B is syntactically incorrect and doesn't implement the time window. Option D is truncating by minute and then groups across that resulting in incorrect aggregation. The correct function is 'DATE DIFF, not 'DATE TRUNC and the logic for window is incorrect. Option E will result in errors as TUMBLE_START requires view_time to be timestamp_ltz or timestamp_tz data type.


NEW QUESTION # 57
When maintaining reports and dashboards, why is it crucial to build automated and repeatable tasks?

Answer: D

Explanation:
Automated tasks reduce manual efforts, ensuring consistency in reports and dashboards.


NEW QUESTION # 58
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