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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Transformation and Data Modeling	22%-30%	- Design data models 1. Data vault models 2. Star schema design 3. Snowflake schema design - Transform data using SQL 1. Window functions 2. Common Table Expressions (CTEs) 3. QUALIFY clauses 4. PIVOT/UNPIVOT operations
Data Presentation and Data Visualization	28%-29%	- Integrate with BI tools 1. Tableau integration 2. Other partner visualization tools 3. Power BI integration - Create dashboards 1. Snowsight dashboards 2. Present analytical results
Data Ingestion and Data Preparation	15%-20%	- 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 - Implement data processing solutions 1. Automate and implement data pipelines (scheduling) 2. Respond to processing failures 3. Cleanse, conform, and enrich data 4. Use logging and monitoring solutions (auditing, data lineage) - Use a collection system to retrieve data 1. Retrieve data from structured sources (CSV) 2. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 3. Synthetic Data Generation 4. Retrieve data from unstructured sources - 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 - Prepare data and load into Snowflake 1. Load files using Snowsight 2. Load data from external/internal stages into a table - Perform data discovery to identify what is needed from available datasets 1. Evaluate required transformations (table joins, set operations, ASOF JOINS) 2. Determine the level of data granularity required 3. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 4. Identify elements required for business goals using BI reports or SQL analysis 5. Query tables to assess data elements and statistics maintained by Snowflake
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 (Q28-Q33):

NEW QUESTION # 28
You are tasked with identifying potential data sources for a new marketing analytics dashboard. The dashboard needs to provide insights into customer behavior across various touchpoints. Which of the following would be the MOST appropriate data sources to consider?

A. Website clickstream data stored in AWS S3 buckets in Parquet format.
B. IoT sensor data containing temperature readings.
C. Salesforce data containing customer interactions and sales opportunities.
D. Social media activity data ingested via a third-party API and stored in a relational database.
E. Database containing HR employee data.

Answer: A,C,D

Explanation:
Options A, B, and C are the most relevant data sources for a marketing analytics dashboard focused on customer behavior. Website clickstream data (A) provides insights into user interactions on the website. Social media activity data (B) offers insights into customer sentiment and engagement. Salesforce data (C) provides information about customer interactions and sales opportunities. HR employee data (D) and IoT sensor data (E) are less relevant to customer behavior and marketing analytics.

NEW QUESTION # 29
Your organization receives daily updates to a product catalog in Avro format. The Avro schema evolves frequently, adding and removing fields. You need to ingest these Avro files into Snowflake. Which of the following strategies will BEST handle schema evolution while minimizing data loading complexity?

A. Create a new Snowflake table for each Avro schema version. Maintain a view that unions all tables to provide a consistent interface.
B. Define a fixed Snowflake table schema with a limited set of commonly used fields. Ignore any additional fields in the Avro files during loading.
C. Load the Avro files into a VARIANT column in Snowflake and use SQL to extract the required fields. Periodically update the table schema as the Avro schema evolves.
D. Load the Avro files into a CSV format after flattening the Avro Schema.
E. Use Snowflake's Schema Detection feature with the COPY command into a table with a VARIANT column. Then, use the 'LATERAL FLATTEN' function to extract the fields and load them into a relational table with the correct data types. Automate the schema creation/alteration of the relational table based on the inferred schema from the VARIANT column.

Answer: E

Explanation:
Option D is the best approach. Loading the Avro data into a VARIANT column first allows you to capture all the data, regardless of schema evolution. Using the 'LATERAL FLATTEN' function allows extraction of fields. Utilizing Snowflake's schema detection further automates the process. Option A requires manual schema updates, which is less scalable. Option B creates a management overhead with multiple tables and views. Option C loses data. Option E requires external flattening which creates additional complexity.

NEW QUESTION # 30
A data analyst needs to process a large JSON payload stored in a VARIANT column named 'payload' in a table called 'raw events' The payload contains an array of user sessions, each with potentially different attributes. Each session object in the array has a 'sessionld' , 'userld' , and an array of 'eventS. The events array contains objects with 'eventType' and 'timestamp'. The analyst wants to use a table function to flatten this nested structure into a relational format for easier analysis. Which approach is most efficient and correct for extracting and transforming this data?

A. Create a recursive UDF (User-Defined Function) in Python to traverse the nested JSON and return a structured result, then call this UDF in a SELECT statement.
B. Load the JSON data into a temporary table, then write a series of complex SQL queries with JOINs and UNNEST operations to flatten the data.
C. Use LATERAL FLATTEN with multiple levels of nesting, specifying 'path' for each level and directly selecting the desired attributes.
D. Employ a combination of LATERAL FLATTEN and Snowpark DataFrames, using LATERAL FLATTEN to partially flatten the JSON and then Snowpark to handle the remaining complex transformations and data type handling.
E. Utilize a Snowpark DataFrame transformation with multiple 'explode' operations and schema inference to flatten the nested structure and load data into a new table.

Answer: C

Explanation:
Option A is the most efficient and Snowflake-native approach. LATERAL FLATTEN is optimized for handling nested data structures within Snowflake. While other options might work, they introduce overhead (UDF execution), are less efficient (temporary tables and complex SQL), or rely on external frameworks (Snowpark), making them less suitable for this scenario. Specifying the path ensures specific fields are targeted, avoiding unnecessary processing of irrelevant data. LATERAL flatten allows you to join the output of a table function with each row of the input table. This is essential to maintain the context (e.g., userId) from the outer table.

NEW QUESTION # 31
Your organization is migrating a large on-premise data warehouse (100 TB) to Snowflake. The existing data warehouse contains complex data transformations implemented in stored procedures. During the migration, you need to minimize downtime and accurately estimate the data volume transferred to Snowflake. You decide to use a hybrid approach with both batch and incremental loading. Which of the following strategies would be MOST appropriate?

A. Use Snowflake's Data Exchange to directly replicate the on-premise data warehouse to Snowflake. Rely on Data Exchange's built-in monitoring to track the data volume transferred.
B. Perform a full batch load of the historical data using Snowpipe from a cloud storage location. After the initial load, implement a change data capture (CDC) solution using a third-party tool (e.g., Debezium, Qlik Replicate) to incrementally load changes into Snowflake.
C. Utilize Snowflake Connectors (e.g., Kafka connector) to stream data directly from the on-premise data warehouse to Snowflake. Implement a data replication tool to handle the initial data load and ongoing synchronization.
D. Implement a custom ETL pipeline using Apache Spark to extract data from the on-premise data warehouse, perform transformations, and load the data into Snowflake using the Snowflake JDBC driver. Continuously run Spark jobs to keep the data synchronized.
E. Create external tables pointing to the on-premise data warehouse. Run queries directly against the external tables to transform and load the data into Snowflake. Once all data is loaded, drop the external tables. Use 'TABLE_SIZE function for sizing calculation.

Answer: B

Explanation:
Option B is the most appropriate strategy. It allows for a faster initial load using Snowpipe, leveraging the scalability of cloud storage. Implementing a CDC solution minimizes downtime and ensures data synchronization after the initial load. The combined approach addresses both the large data volume and the need for continuous updates.

NEW QUESTION # 32
When summarizing large data sets using Snowsight dashboards, what distinguishes them in handling complex data structures?

A. Snowsight dashboards can't handle complex data structures efficiently.
B. They simplify complex data structures for better comprehension.
C. They limit data representation options for complex structures.
D. Snowsight dashboards only present textual summaries.

Answer: B

Explanation:
Snowsight dashboards aid in simplifying complex data structures for better comprehension.

NEW QUESTION # 33
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