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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q41-Q46):

NEW QUESTION # 41
In Snowflake, how does the implementation of primary keys for tables contribute to data integrity?

A. Enforces uniqueness in table rows
B. Requires data redundancy
C. Limits data access for users
D. Only works on specific data types

Answer: A

Explanation:
Primary keys enforce uniqueness in table rows, ensuring data integrity by preventing duplicate entries within a table.

NEW QUESTION # 42
You are tasked with loading multiple CSV files from an external stage into a Snowflake table. The files have the following naming convention: HHMlSS.csv' (e.g., 'data_20240101 120000.csv'). You need to load all the files that were created on or after '2024-01-01' into the 'target_table'. Which of the following approaches can be used to load those data?

A. Using a single COPY INTO statement with a pattern match against METADATA$FILENAM The matching logic is done inline with the COPY INTO statement using regex.
B. Using a loop with dynamic SQL to construct COPY INTO statements for each matching file based on the date substring.
C. Using a single COPY INTO statement with a wildcard pattern in the file path and filtering in a subsequent INSERT INTO statement using a WHERE clause on the filename derived from METADATA$FILENAME.
D. Using a Snowpipe with a file notification integration, configuring the notification to trigger only for files created on or after '2024-01-01'.
E. Using a Snowflake task that runs daily, retrieves all the file names from the stage, filters the file names based on created date and loops through each filtered file name to load them

Answer: A,C

Explanation:
Option B is a viable solution. COPY INTO can be combined with METADATA$FILENAME filtering to load files matching a criteria. Option E provides a better, more efficient solution that leverages the 'METADATA$FILENAME' pseudo-column to filter files directly within the 'COPY INTO' statement using regular expressions or string functions to extract and compare the date from the filename. Option A will work but is very inefficient. Option C will work, but complex to maintain. Option D is more complex to setup than option E. Snowpipe generally does not accept a single COPY command but rather an ongoing copy operation

NEW QUESTION # 43
Which statistical method is commonly used in forecasting based on historical data?

A. Simple data aggregation
B. Data normalization
C. Inferential statistics
D. Regression analysis

Answer: D

Explanation:
Regression analysis is frequently employed for forecasting based on historical data, predicting future trends based on past patterns.

NEW QUESTION # 44
When enriching data with Snowflake Marketplace, what role do data shares play in joining external data with existing datasets?

A. Data shares facilitate secure data exchange between parties.
B. Data shares only work with Snowflake-provided datasets.
C. They limit the types of data that can be joined.
D. They restrict access to external data.

Answer: A

Explanation:
Data shares enable secure data exchange, allowing joining external data with existing datasets.

NEW QUESTION # 45
You are tasked with loading data from a REST API that returns data in a deeply nested JSON format. The API requires authentication via an API key passed in the header. The JSON response contains an array of objects, each representing a customer order. You need to load only orders placed in the last 7 days. Which of the following steps and techniques would you use in Snowflake to achieve this?

A. Use Snowpipe with a continuous data loading pipeline, configure an external function to authenticate and load data into a temporary table then execute merge statement to apply it to the main table
B. Use a Python UDF to call the REST API, extract the relevant data, and load it into a Snowflake table.
C. Use Snowflake's 'COPY INTO' command with a preprocessor function to transform the JSON data and filter orders placed in the last 7 days. Store the API key in the preprocessor function.
D. Create a task with 'SYSTEM$STREAM' object to extract the data from API and apply it into Snowflake tables. Use 'SECRET object to store API Key.
E. Create an external function to call the REST API using API Integration object, filter the orders based on the timestamp within the UDF, and return the filtered data to Snowflake to be loaded into a table. Utilize secure secrets for API Key Storage.

Answer: E

Explanation:
Option B presents the most secure and robust approach. External Functions with API Integration objects allow calling external APIs. The filtering within the UDF ensures that only the required data is loaded, optimizing performance. Secure Secrets are essential for securely storing the API key. Option A is a viable solution but it requires careful security management of the API Key within Python code. Options C is incorrect since COPY INTO command doesn't support calling API with API Key in Header and it also require preprocessor function which are hard to implement. Option D is possible but doesn't mention API keys secure store which is less secure. Option E is not possible since 'SYSTEM$STREAM' object don't allow call API for data loading.

NEW QUESTION # 46
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