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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Use Built-in Functions and Create UDFs	10–15%	- User-Defined Functions (UDFs) - Scalar, aggregate, table, system functions
Topic 2: Prepare and Present Data	10–15%	- Align outputs with business requirements - Snowsight dashboards and sharing results - Data visualization and reporting
Topic 3: Perform Simple Data Transformations for Analysis	15–20%	- Handling NULLs and structuring datasets - Views, materialized views, CTEs - Data cleansing, standardization, type conversion
Topic 4: Prepare and Load Data	15–20%	- File formats: CSV, JSON, Parquet, Avro - External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe
Topic 5: Build and Troubleshoot Advanced SQL Queries	20–25%	- Complex joins, subqueries, window functions - Query optimization and troubleshooting - Semi-structured data processing
Topic 6: Perform Predictive Analysis	5–10%	- Forecasting and predictive modeling - Using Snowflake ML and built-in analytics
Topic 7: Perform Descriptive and Diagnostic Analysis	10–15%	- Exploratory and ad-hoc analysis - Statistical summarization and trend analysis - Anomaly detection and root cause analysis

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q10-Q15):

NEW QUESTION # 10
When selecting and implementing an effective data model, what considerations are crucial for ensuring its suitability for BI requirements? (Select all that apply)

A. Performance and ease of maintenance
B. Extensive data denormalization
C. Conformity to specific database standards only
D. Scalability and flexibility

Answer: A,D

Explanation:
Effective data models should ensure scalability, flexibility, good performance, and ease of maintenance to meet BI requirements effectively.

NEW QUESTION # 11
A data analyst needs to perform an exploratory ad-hoc analysis on a large customer order dataset in Snowflake to identify potential fraud. The dataset contains sensitive Personally Identifiable Information (PII). Which of the following approaches BEST balances the need for ad-hoc analysis with data security and compliance?

A. Use Snowflake's data sharing capabilities to share a subset of the production data with the analyst, applying row-level security to filter out sensitive data.
B. Mask the PII columns using SnoMlake's Dynamic Data Masking feature in the production environment and grant the analyst access to the production database.
C. Export a sample of the data to a local environment, removing all PII, and provide the sample to the analyst.
D. Grant the analyst direct access to the production database with no restrictions, relying on the analyst's ethical responsibility.
E. Create a clone of the production database and grant the analyst full access to the clone.

Answer: B

Explanation:
Dynamic Data Masking allows the analyst to perform ad-hoc analysis on the production data without exposing sensitive PII. Cloning with full access is risky. Exporting a sample may not be representative. Unrestricted access is a security violation. Data sharing with row-level security can be complex and might still inadvertently expose data.

NEW QUESTION # 12
You are tasked with diagnosing a performance bottleneck in a daily ETL process that loads data into a Snowflake table called 'SALES DATA'. The ETL process has been running slower than usual for the past week. You suspect a change in the source data volume or distribution. Which of the following Snowflake features and SQL queries would be MOST helpful in identifying the root cause?

A. Use Snowflake's Time Travel feature to compare the size and structure of the 'SALES DATA table at different points in time, specifically before and after the performance degradation started.
B. Analyze the 'SALES DATA' table's clustering keys and statistics using 'SHOW TABLES LIKE 'SALES DATA';' and 'DESCRIBE TABLE SALES DATA;' to determine if the data distribution has changed significantly, potentially leading to inefficient query performance.
C. Query Snowflake's INFORMATION SCHEMA.QUERY HISTORY view to compare the execution times of recent ETL runs with historical averages, filtering by query ID or user.
D. Use Snowflake's Query Profile feature to analyze the execution plan of the ETL queries and identify the stages consuming the most time.
E. Run 'SELECT COUNT( ) FROM SALES DATA;' to check total record count and compare against historical values.

Answer: A,B,C,D

Explanation:
Options A, B, C, and D are all helpful in diagnosing the bottleneck. The query profile (A) pinpoints specific query stages, query history (B) reveals performance trends, table statistics (C) indicate data skewness, and Time Travel (D) allows for data structure comparisons. While E is useful as an initial check, it is not sufficient to determine the root cause alone.

NEW QUESTION # 13
What considerations are essential when identifying the volume of data to be collected in a collection system? (Select all that apply)

A. Frequency of data analysis
B. Speed of data retrieval
C. Data redundancy requirements
D. Available storage capacity

Answer: A,D

Explanation:
Identifying the volume of data involves considering available storage capacity and the frequency of data analysis.

NEW QUESTION # 14
You are tasked with creating a data model for a global e-commerce company in Snowflake. They have data on customers, products, orders, and website events. They need to support complex analytical queries such as 'What are the top 10 products purchased by customers in the US who have visited the website more than 5 times in the last month?' The data volumes are very large, and query performance is critical. Which of the following data modeling techniques and Snowflake features, used in combination, would be MOST effective?

A. A fully normalized relational model with primary and foreign key constraints, combined with Snowflake's automatic query optimization.
B. A data vault model, combined with Snowflake's search optimization service on the hub tables.
C. A wide, denormalized table containing all customer, product, order, and event data, combined with Snowflake's zero-copy cloning for data backups.
D. A star schema with fact and dimension tables, combined with materialized views to pre-aggregate data and clustering on dimension keys in the fact table.
E. A star schema with fact and dimension tables, combined with clustering the fact table on a composite key of customer ID and product ID.

Answer: D,E

Explanation:
Options B and E are the most effective. A star schema (B and E) is well-suited for analytical workloads. Clustering the fact table on customer and product IDs (B) helps improve query performance when filtering on those dimensions. Materialized views (E) provide pre-aggregated data for common queries, further boosting performance. Normalization (A) can lead to too many joins. Data Vault (C) is complex and may not be necessary. A wide, denormalized table (D) can be difficult to manage and maintain, and zero-copy cloning is for backup, not performance. Clustering on dimension keys in the fact table works best when coupled with a star schema and the keys are frequently used as a filter.

NEW QUESTION # 15
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