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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Use Built-in Functions and Create UDFs	10–15%	- Scalar, aggregate, table, system functions - User-Defined Functions (UDFs)
Prepare and Present Data	10–15%	- Snowsight dashboards and sharing results - Align outputs with business requirements - Data visualization and reporting
Perform Predictive Analysis	5–10%	- Using Snowflake ML and built-in analytics - Forecasting and predictive modeling
Perform Descriptive and Diagnostic Analysis	10–15%	- Anomaly detection and root cause analysis - Exploratory and ad-hoc analysis - Statistical summarization and trend analysis
Build and Troubleshoot Advanced SQL Queries	20–25%	- Complex joins, subqueries, window functions - Query optimization and troubleshooting - Semi-structured data processing
Prepare and Load Data	15–20%	- External tables and data validation - File formats: CSV, JSON, Parquet, Avro - Data ingestion methods: COPY INTO, stages, Snowpipe
Perform Simple Data Transformations for Analysis	15–20%	- Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q14-Q19):

NEW QUESTION # 14
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.
B.
C.
D.
E. None of the above.

Answer: A

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 # 15
In the context of data processing solutions, how does handling processing failures contribute to ensuring data reliability?

A. Increases data redundancy
B. Prevents data loss or corruption
C. Ensures real-time data availability
D. Reduces system downtime

Answer: B

Explanation:
Handling processing failures ensures data reliability by preventing data loss or corruption, maintaining the integrity of processed data.

NEW QUESTION # 16
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 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'.
B. 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.
C. 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.
D. 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' .
E. 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.

Answer: B,E

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 # 17
What considerations are essential when identifying the volume of data to be collected in a collection system? (Select all that apply)

A. Data redundancy requirements
B. Available storage capacity
C. Speed of data retrieval
D. Frequency of data analysis

Answer: B,D

Explanation:
Identifying the volume of data involves considering available storage capacity and the frequency of data analysis.

NEW QUESTION # 18
A retail company wants to understand the relationship between promotional campaigns and sales uplift across different store locations and product categories. You have the following Snowflake tables: 'SALES': 'transaction id', 'store id', 'product_category', 'sale date', 'sale_amount' 'PROMOTIONS': 'promotion id', 'store id', 'product category', 'promotion start date', promotion_end_date', 'discount_percentage' Which analytical approach and corresponding SQL query would be MOST effective in determining if specific promotional campaigns consistently result in a statistically significant sales uplift, considering potential variations across different store locations and product categories? Assume you want to compare sales during the promotion period to a control period (before the promotion). (Select TWO)

A. Use a simple linear regression model in a Snowflake UDF (User-Defined Function) to predict sales based on the presence or absence of a promotion, without accounting for store or product category fixed effects.
B. Calculate the average sales amount for each store and product category during promotion periods and compare it to the overall average sales amount across all periods using a t-test or similar statistical test to determine significance. This would involve exporting data into a statistical tool.
C. Create a complex query that joins SALES and PROMOTIONS and calculates the percentage increase in sales during the promotion period compared to the average sales for the same store and product category in the 3 months prior, ignoring the potential for seasonality and other confounding factors.
D. Run a series of AIB tests by randomly assigning different discount percentages to different stores and product categories during the promotion period and track the resulting sales uplift, using a Mann-Whitney U test for statistical significance.
E. Employ a difference-in-differences (DID) approach, comparing the change in sales from the control period to the promotion period for the 'treatment group' (stores/product categories with promotions) relative to the change for a 'control group' (stores/product categories without promotions). This requires careful identification of suitable control groups.

Answer: B,E

Explanation:
Options A and C are the most effective. Option A utilizes t-tests to assess the statistical significance of sales during promotion periods versus the overall average. Combining statistical analysis with Snowflake data extraction provides insightful results. Option C proposes the Difference-in-Differences (DID) approach which is very effective. It uses a control group to account for external factors that may have also influenced sales. Comparing treated (promotion stores) and controlled store to find the diff in diff provides statistically significant evidence. Options B and E are less rigorous. B doesn't account for important fixed effects and E doesn't consider seasonality and confounding factors. Option D suggests an AIB test which is not practical as sales can not be assigned randomly during a promotion.

NEW QUESTION # 19
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