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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
When customizing data presentations in dashboards through filtering and editing techniques, what benefits do these methods offer? (Select all that apply)

A. Enhanced data relevancy
B. Simplified data representation
C. Limited data exploration
D. Improved data accuracy

Answer: A,D

Explanation:
Customization through filtering and editing enhances data accuracy and relevancy in dashboards.

NEW QUESTION # 14
A company receives daily CSV files containing customer order data'. Each file contains a header row and is compressed using GZIP.
The files are landed in an AWS S3 bucket. The company wants to automate the data ingestion into a Snowflake table named 'orders table'. The requirements are: 1. Automated ingestion: New files should be automatically ingested as they arrive in the S3 bucket. 2. Data validation: Records with invalid dates or missing product IDs should be rejected and logged for review. 3. Data transformation: The column (string format 'YYYY-MM-DD') needs to be converted to a DATE data type, and a new column 'order _ year' needs to be derived from the 'order_date'. Which combination of Snowflake features and configurations provides the MOST efficient and reliable solution to meet these requirements?

A. Create an external table pointing to the S3 bucket. Use a stream on the external table to track changes and a task to periodically move the new data into the 'orders_table' while performing the necessary transformations and validations. This could also be achieved using Dynamic Tables.
B. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that utilizes a user-defined function (UDF) written in Python to perform complex data validation and transformation before loading the data into the 'orders_table'. Set 'ON_ERROR = 'SKIP_FILEP to avoid loading erroneous data.
C. Create a Snowpipe that points to the S3 bucket. Use a COPY INTO statement with 'VALIDATE(O)' and a BEFORE trigger to invoke a stored procedure that validates the data against a set of rules. Use a stored procedure to transform the data into 'orders_table' .
D. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that performs the date conversion using TO DATE() and extracts the order year using YEAR(). Configure the COPY INTO statement with "ON_ERROR = 'CONTINUE" and a validation table to log rejected records.
E. Create a Snowpipe that points to the S3 bucket with a COPY INTO statement that includes 'ON ERROR = 'SKIP_FILE". Use a downstream task to periodically validate and transform the data in the 'orders_table'.

Answer: A,D

Explanation:
Options B and C offer the best combination of features to address the requirements effectively. Option B leverages Snowpipe's COPY INTO statement to directly convert the date, calculate order year, and handle errors by continuing the load and logging invalid records into a validation table. This maximizes efficiency and ensures that valid data is ingested quickly. ON ERROR = 'CONTINUE' is better than SKIP FILE since it is preferable to ingest valid data in file even some has issues. Option C uses external tables combined with streams and tasks or dynamic tables which is an alternative to Snowpipe and COPY INTO and also provides automatic ingestion and transformation capabilities. Option A is less effective because it does not provide a mechanism to capture and log errors from the copy process; skipping files provides no insight to the validity of data. Option D is not the most efficient. While UDFs can handle complex transformations, relying solely on them for all validation and transformation steps can lead to performance bottlenecks and introduce maintenance overhead; Also setting ON_ERROR = 'SKIP_FILE' isn't a great pattern if you want to ingest partial data. Option E's BEFORE trigger might add significant overhead since Snowflake triggers have limitations.

NEW QUESTION # 15
How do constraints contribute to ensuring data integrity in Snowflake?

A. Constraints restrict data import from external sources.
B. They enforce data security measures.
C. They impose rules for maintaining data accuracy and consistency.
D. Constraints ensure redundant data storage.

Answer: C

Explanation:
Constraints in Snowflake impose rules for maintaining data accuracy and consistency, ensuring data integrity.

NEW QUESTION # 16
How does Snowflake's support for materialized views contribute to query optimization and data analysis?

A. Materialized views increase data storage requirements only
B. Materialized views improve query performance by pre-computing results
C. Materialized views limit query optimization possibilities
D. Materialized views restrict data access across warehouses

Answer: B

Explanation:
Materialized views in Snowflake enhance query performance by pre-computing and storing query results, optimizing data analysis by providing faster access to aggregated data.

NEW QUESTION # 17
You are preparing to load data from an internal stage that contains multiple CSV files into a Snowflake table. You need to ensure that only files matching a specific pattern are loaded (e.g., files starting with 'sales_'). You also need to transform the date column (located in the third position of each CSV file) from 'YYYYMMDD' to 'YYYY-MM-DD' format during the load. The target table 'SALES has columns 'product_id', and 'region'. Which combination of options correctly filters the files and transforms the date format during the load process?

A. Option D
B. Option A
C. Option E
D. Option B
E. Option C

Answer: A

Explanation:
Option D is correct. It uses the 'PATTERN' parameter to filter files starting with 'sales_' and ending with '.csv'. It then uses the TRANSFORM AS SELECT clause to select and transform the data. Inside the select clause, it correctly uses 'substr' to reformat the date and 'TO_DATE to convert the re-formatted string into a DATE datatype. The 'ON_ERROR = 'SKIP_FILE" is a good practice for handling potential errors. Option A has an invalid parameter declaration within the COPY INTO command and does not use PATTERN, making the filter inaccurate. Option B attempts to define date format at file format level, but date transformatinon is required and FILES filter declaration is invalid. Option C uses 'TRANSFORMATION' instead of 'TRANSFORM AS SELECT , which is incorrect and will cause an error. Option E attempts to define DATE FORMAT with the wrong TYPE and is missing concatination of DATE string.

NEW QUESTION # 18
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