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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Transformation and Data Modeling	22%-30%	- Transform data using SQL 1. Common Table Expressions (CTEs) 2. QUALIFY clauses 3. PIVOT/UNPIVOT operations 4. Window functions - Design data models 1. Snowflake schema design 2. Star schema design 3. Data vault models
Topic 2: Data Presentation and Data Visualization	28%-29%	- Create dashboards 1. Present analytical results 2. Snowsight dashboards - Integrate with BI tools 1. Power BI integration 2. Tableau integration 3. Other partner visualization tools
Topic 3: Data Ingestion and Data Preparation	15%-20%	- Use a collection system to retrieve data 1. Synthetic Data Generation 2. Retrieve data from unstructured sources 3. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 4. Retrieve data from structured sources (CSV) - Perform data discovery to identify what is needed from available datasets 1. Query tables to assess data elements and statistics maintained by Snowflake 2. Identify elements required for business goals using BI reports or SQL analysis 3. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 4. Evaluate required transformations (table joins, set operations, ASOF JOINS) 5. Determine the level of data granularity required - 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 - Prepare data and load into Snowflake 1. Load data from external/internal stages into a table 2. Load files using Snowsight - Implement data processing solutions 1. Automate and implement data pipelines (scheduling) 2. Use logging and monitoring solutions (auditing, data lineage) 3. Respond to processing failures 4. Cleanse, conform, and enrich data - 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
Topic 4: Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Aggregate functions 2. Snowflake-specific analytical features 3. Time-series analysis

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

NEW QUESTION # 18
You are tasked with creating a stored procedure in Snowflake to perform data cleansing on a table named 'CUSTOMER DATA'. The procedure should: 1) Remove rows where the 'EMAIL' column is NULL or empty. 2) Standardize the 'PHONE NUMBER' column by removing all non-numeric characters and ensuring it's exactly 10 digits long. 3) Return the number of rows removed due to invalid emails and the number of rows modified due to phone number standardization. Assume the table already exists and contains columns 'CUSTOMER (INT), (VARCHAR), and 'PHONE NUMBER (VARCHAR). Which of the following code snippets correctly implements this stored procedure? The procedure should use exception handling to gracefully handle errors, returning -1 for both counts if any error occurs.

A. None of the above.
B.
C.
D.
E.

Answer: C

Explanation:
Option A is correct because it uses SQL to perform the data cleansing tasks, correctly utilizes 'SQLROWCOUNT' to capture the number of affected rows, and returns the results as a VARIANT OBJECT. It also includes proper exception handling. Options B, C, and D have errors in syntax or logic regarding return types, variable declaration, or how to retrieve row counts. Specifically, using Javascript or returning an ARRAY/TABLE when VARIANT is more flexible in this scenario.

NEW QUESTION # 19
You are designing a data ingestion pipeline for IoT sensor data'. Sensors transmit data in JSON format every 5 seconds. The volume of data is expected to grow exponentially. The business requires both real-time dashboards and historical trend analysis. Which of the following strategies should you employ to address these requirements, particularly focusing on optimizing for both ingestion frequency and cost?

A. Use Snowpipe to ingest data into a raw landing table, and then use Snowflake tasks to transform and load the data into separate tables optimized for real-time dashboards and historical analysis.
B. Utilize an external stream processing engine to pre-aggregate the data into time windows (e.g., I-minute, I-hour) before ingesting into Snowflake using Snowpipe.
C. Employ a combination of Snowpipe for near real-time data ingestion into a raw table, and then use Snowflake's Search Optimization Service for faster queries.
D. Implement Snowpipe for initial data ingestion, complemented by Snowflake's clustering feature based on timestamp to optimize historical analysis queries. And employ a stream processing engine to perform time window pre-aggregation.
E. Ingest data directly into a single Snowflake table using Snowpipe with JSON data type. Create separate materialized views for real-time dashboards and historical trend analysis.

Answer: A,D

Explanation:
This question has multiple correct answers. Option B and E addresses the need for optimizing both ingestion frequency and cost. Option B involves using Snowflake tasks for transformations, which allows for separating data for real-time and historical analysis. This optimizes query performance for both use cases. Using only Snowpipe directly into a single table is simple but doesn't provide optimization for the different query patterns, while the other options are less efficient or don't fully address the dual requirements. Option E complements using Snowpipe by using Snowflake clustering features and employing stream processing engine to perform pre-aggregation. Snowflake clustering optimize the speed of historical analysis, reducing cost of scanning large datasets.

NEW QUESTION # 20
How do Snowsight dashboards facilitate the presentation of data for business use analyses?

A. Snowsight limits data representation options, hindering analyses.
B. Snowsight doesn't support visual data representation.
C. They enable diverse data representation for effective analyses.
D. Snowsight dashboards are exclusively text-based, limiting analyses.

Answer: C

Explanation:
Snowsight dashboards enable diverse data representation for effective analyses in business use cases.

NEW QUESTION # 21
A financial institution needs to build a dashboard to monitor fraudulent transactions. They have transaction data (amount, timestamp, location, merchant category), customer data (age, income, credit score), and fraud flags. They want to identify patterns and correlations that indicate potentially fraudulent activity. Which of the following visualizations and Snowflake features, when used in combination, would be MOST effective for this purpose? (Select TWO)

A. Export the transaction data to a data lake and use Spark to perform fraud detection.
B. Use Snowflake's secure data sharing to share the raw transaction data with a third-party analytics vendor.
C. Develop an interactive dashboard using Power BI connected to Snowflake, incorporating a geographical map showing transaction locations, a time series chart of transaction volume, and a scatter plot of transaction amount vs. customer credit score. Use anomaly detection algorithms in Snowflake (via Snowpark or stored procedures using Python) to highlight unusual transactions. Implement User defined table Function in Power BI to generate more data.
D. Utilize a heatmap visualization in a Tableau dashboard to show the correlation between merchant category, transaction amount, and fraud flag. Implement interactive filters for customer age, income, and location. Implement Snowflake Row Access Policy to restrict data to specific users.
E. Create a static report showing the total number of fraudulent transactions per month, using Snowflake's aggregate functions.

Answer: C,D

Explanation:
Options B and D are the most effective. Option B utilizes an interactive dashboard with Power BI, incorporating geographical, temporal, and scatter plot visualizations to identify patterns. Anomaly detection algorithms in Snowflake further enhance the detection capabilities. Option D uses a heatmap to show correlations between key variables and implements interactive filters for detailed exploration. Implementing Row Access Policy would control data access, adding a security layer on top of sensitive financial data. Option A is not related to visualization or identifying correlations. Option C provides only a high-level summary. Option E involves exporting data outside Snowflake, which is less efficient and potentially less secure than using Snowflake's built-in capabilities.

NEW QUESTION # 22
A healthcare organization uses Snowflake to store patient data and needs to create a dashboard to monitor key performance indicators (KPIs) such as patient readmission rates, average length of stay, and patient satisfaction scores. The dashboard should allow analysts to filter data by hospital, department, and time period. Considering data governance and security best practices, which of the following approaches should you implement to ensure that analysts only have access to the data relevant to their roles and responsibilities, while also optimizing dashboard performance and maintainability? Choose all that apply.

A. Grant all analysts full access to the entire patient data warehouse and rely on the dashboard's built-in filtering capabilities to restrict data visibility.
B. Create separate Snowflake roles for different analyst groups (e.g., hospital analysts, department analysts) and grant each role access only to the specific views or tables containing the data they need. Apply row-level security policies to filter data based on the user's role.
C. Create a set of stored procedures that encapsulate the logic for retrieving and aggregating the KPI data. Grant analysts execute privileges on these stored procedures, but not direct access to the underlying tables.
D. Implement data masking techniques to redact sensitive patient information (e.g., names, addresses) within the dashboard visualizations. Ensure that data masking policies are applied consistently across all dashboards and reports.
E. Build a single view that joins all necessary tables and use the view to power the dashboards. Apply role-based access control to the view.

Answer: B,C,D

Explanation:
Option A enforces role-based access control at the data layer, preventing unauthorized access. Option C provides a controlled interface for data access, enhancing security and simplifying data governance. Option D protects sensitive data within the dashboard. Granting full access (B) violates security principles. Single view can become unmanageable.

NEW QUESTION # 23
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