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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q52-Q57):

NEW QUESTION # 52
You are working with a large dataset of website clickstream data'. You need to perform several data transformation steps, including filtering, aggregating, and joining with other tables. You want to ensure that your data cleaning and transformation process is idempotent, meaning that running the same transformation pipeline multiple times will produce the same result, even if the input data changes slightly. Which of the following strategies contribute to building an idempotent data transformation pipeline in Snowflake? (Select all that apply)

A. Using temporary tables instead of permanent tables for transformation.
B. Relying solely on 'INSERT INTO' statements for data loading.
C. Using clones of source tables as input to the transformation pipeline, ensuring that the source data remains unchanged during the process.
D. Using 'MERGE statements with appropriate matching conditions to update or insert records based on the latest data.
E. Always truncating the target table before inserting transformed data.

Answer: C,D,E

Explanation:
Options A, B, and D are correct. Truncating the target table (A) before each run ensures a clean slate. Using 'MERGE statements (B) handles both updates and inserts, ensuring that the target table reflects the latest data state regardless of previous runs. Using clones (D) protects the original source data from unintended modifications during the transformation process, contributing to idempotency. Option C using just INSERT INTO is not idempotent as it will duplicate data on rerun. Option E, while useful for performance and cleanup, doesn't directly contribute to idempotency. The use of temporary tables alone does not guarantee idempotency if the operations performed on them are not idempotent. You might still see inconsistencies in data if the operations are not carefully designed. Truncating permanent table before adding data using MERGE is more reliable and ensures consistent result even on multiple runs.

NEW QUESTION # 53
A company stores web analytics data in a Snowflake table named 'WEB EVENTS. This table includes a 'USER ID column, a 'TIMESTAMP' column indicating when the event occurred, and a 'EVENT TYPE column that captures the type of event (e.g., 'page_view', 'add_to_cart', 'purchase'). The data analysts want to enrich this data to identify the first and last event times for each user. Which Snowflake features or functions would be MOST appropriate and efficient for achieving this enrichment?

A. Using a simple GROUP BY clause on 'USER ID to find the minimum and maximum timestamp.
B. Using a correlated subquery to find the minimum and maximum timestamp for each user in the 'WEB EVENTS' table.
C. Using window functions such as FIRST _ VALUE and 'LAST_VALUE partitioned by 'USER_ID and ordered by 'TIMESTAMP' to find the first and last event times.
D. Creating a stored procedure that iterates through each user ID and finds the minimum and maximum timestamp using separate queries.
E. Using a lateral view combined with a table function to find the first and last event times.

Answer: C

Explanation:
Window functions are the most efficient approach for calculating aggregate values (like minimum and maximum) within partitions (in this case, per user) without requiring self-joins or subqueries. Correlated subqueries can be inefficient for large datasets. Stored procedures with iteration are generally slower than set-based operations. Lateral views are more suitable for exploding array structures, not for finding min/max values. A simple GROUP BY would provide the overall minimum and maximum, not per user.

NEW QUESTION # 54
You are responsible for loading data into a Snowflake table named 'CUSTOMER DATA' from a series of compressed JSON files located in a Google Cloud Storage (GCS) bucket. The data volume is significant, and the loading process needs to be as efficient as possible. The JSON files are compressed using GZIP, and they contain a field called 'registration date' that should be loaded as a DATE type in Snowflake. However, some files contain records where the 'registration_date' is missing or has an invalid format. Your goal is to load all valid data while skipping any files that contain invalid dates, and log any files that contain invalid records. You want to choose the most efficient approach. Which of the following options represents the best strategy to achieve this?

A. Use Snowpipe with a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP'. Configure error notifications for the pipe and handle errors manually.
B. Create a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP. Use a COPY INTO command with a transformation function 'TRY TO DATE(registration_date)' and 'ON ERROR = SKIP FILE. Implement a separate process to validate the loaded data for NULL 'registration_date' values.
C. create a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP. Use a COPY INTO command with 'ON_ERROR = SKIP_FILE. Implement a scheduled task to query the COPY HISTORY view to identify any skipped files and manually investigate the errors.
D. create a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP'. Use a COPY INTO command with a transformation function 'TRY Configure the 'CUSTOMER DATA' table with a default value for 'registration_date' and use 'ON ERROR = CONTINUE'.
E. Create a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP. Use a COPY INTO command with a transformation function 'TO DATE(registration_datey and SON ERROR = CONTINUE. Use a validation table to store rejected records.

Answer: D

Explanation:
The correct answer is E. Using gracefully handles invalid dates by returning NULL, which can be managed using a default value on the target table. 'ON ERROR = CONTINUE' ensures the loading process doesn't halt. Combining this with a default value provides for a fast, efficient load. Option A skips the entire file, which is not desired if only some records are invalid. Option B will halt the load process if the target field cannot accept a value. Option C is valid but requires a separate process. Option D makes the manual handling more complex since Snowpipe is designed for near real time instead of batch. E is the best option as all invalid fields will be populated with the default value and load will be unaffected.

NEW QUESTION # 55
When cleaning data, what role does using clones play in specific use-cases?

A. Clones slow down the data cleaning process significantly
B. Clones aid in isolating and resolving data anomalies
C. Clones are unnecessary for data cleaning tasks
D. Clones help preserve original data for audit purposes only

Answer: B

Explanation:
Clones are beneficial in isolating and resolving data anomalies without impacting the original dataset, facilitating safe data cleaning practices.

NEW QUESTION # 56
How do secure views contribute to data analysis practices in terms of access control and data security?

A. Secure views enhance data security while allowing selective data access.
B. They restrict data access but don't impact data security.
C. Secure views limit data accessibility for improved security.
D. They prevent the creation of materialized views.

Answer: A

Explanation:
Secure views enhance data security while allowing selective data access.

NEW QUESTION # 57
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