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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Prepare and Load Data	15–20%	- External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro
Topic 2: Perform Predictive Analysis	5–10%	- Forecasting and predictive modeling - Using Snowflake ML and built-in analytics
Topic 3: Prepare and Present Data	10–15%	- Align outputs with business requirements - Snowsight dashboards and sharing results - Data visualization and reporting
Topic 4: Use Built-in Functions and Create UDFs	10–15%	- Scalar, aggregate, table, system functions - User-Defined Functions (UDFs)
Topic 5: Build and Troubleshoot Advanced SQL Queries	20–25%	- Semi-structured data processing - Query optimization and troubleshooting - Complex joins, subqueries, window functions
Topic 6: Perform Simple Data Transformations for Analysis	15–20%	- Data cleansing, standardization, type conversion - Handling NULLs and structuring datasets - Views, materialized views, CTEs
Topic 7: Perform Descriptive and Diagnostic Analysis	10–15%	- Anomaly detection and root cause analysis - Statistical summarization and trend analysis - Exploratory and ad-hoc analysis

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
Your organization stores clickstream data in Parquet files in an external stage 's3://your-bucket/clickstreamP. The data includes nested JSON structures representing user activity. You need to create a Snowflake table to query this data efficiently, extracting specific fields from the nested JSON. The challenge is to optimize query performance by leveraging Parquet's columnar storage and schema evolution capabilities. Which of the following approaches offers the BEST combination of performance and flexibility for querying the data in Snowflake, considering potential schema changes in the Parquet files over time?

A. Create an external table with 'AUTO_REFRESH = TRUE and query the Parquet files directly using to access nested fields. Create a view on top of the external table to simplify queries.
B. Load the Parquet data into a VARIANT column in a Snowflake table using 'COPY INTO'. Create a view on top of this table, using JSON EXTRACT_PATH_TEXT to extract specific fields into separate columns.
C. Create an external table with 'AUTO_REFRESH = TRUE and specify a schema on read using 'FILE_FORMAT = (TYPE = PARQUET, STRIP OUTER ARRAY = TRUE, = TRUE)'. Query the external table directly.
D. Create a Snowflake table with clearly defined columns matching the Parquet schema. Use 'COPY INTO' with a transformation to load the data. Update the table definition whenever the Parquet schema changes.
E. Create an external table pointing to the Parquet files. Define a materialized view on top of the external table, extracting the required fields. Refresh the materialized view periodically.

Answer: E

Explanation:
Materialized Views offer the best performance as they pre-compute and store the results, leveraging Snowflake's caching. They also adapt to schema changes in underlying Parquet files (within limits). External tables alone can be slow because of on-the-fly processing. Loading into VARIANT loses the advantage of Parquet's columnar structure. Predefined columns are rigid and don't handle schema evolution well. Creating standard views on external tables also does not provide the pre-computed benefits of the materialized views.

NEW QUESTION # 18
Why are Stored Procedures valuable in data analysis using SQL?

A. They are exclusively used for one-time data operations.
B. They restrict the execution of repetitive tasks, limiting efficiency.
C. Stored Procedures enable custom and repeated data operations, enhancing efficiency.
D. Stored Procedures solely facilitate data visualization.

Answer: C

Explanation:
Stored Procedures aid in data analysis by enabling custom and repeated data operations, enhancing efficiency.

NEW QUESTION # 19
A Data Analyst needs to write a query that will return all projects from a project table and all employees from an employee table. What type of join should be used in this query?

A. Cross join
B. Inner join
C. Left outer join
D. Full outer join

Answer: D

Explanation:
In Data Transformation and Data Modeling, selecting the correct join type determines how the query handles unmatched records from the participating tables. The requirement here is to return all records from both the projects table and the employee table.
A FULL OUTER JOIN (often shortened to FULL JOIN) is designed specifically for this purpose. It combines the results of both a LEFT OUTER JOIN and a RIGHT OUTER JOIN. It returns:
* Rows where there is a match between the project and the employee.
* Rows from the projects table that have no matching employees (padded with NULL in the employee columns).
* Rows from the employees table that are not assigned to any projects (padded with NULL in the project columns).
Evaluating the Options:
* Option A (INNER JOIN) only returns rows where there is a match in both tables. Any projects without employees or employees without projects would be excluded.
* Option C (LEFT OUTER JOIN) would return all projects, but would exclude employees who are not assigned to a project.
* Option D (CROSS JOIN) creates a Cartesian product, matching every single employee with every single project regardless of actual relationships. This would create a massive, redundant dataset and is not what is requested.
* Option B is the 100% correct answer. It ensures total data visibility from both entities, which is often required in data quality audits or comprehensive resource allocation reporting where the analyst needs to see "unlinked" data on both sides of the relationship.

NEW QUESTION # 20
Data clustering is an example of which type of data analysis technique?

A. Predictive analysis
B. Exploratory analysis
C. Prescriptive analysis
D. Descriptive analysis

Answer: A

NEW QUESTION # 21
A marketing team requires a daily report showcasing website traffic, conversion rates, and cost per acquisition (CPA). They want to receive this report as a CSV file attached to an email. The data is stored in a Snowflake table 'WEB ACTIVITY with columns 'DATE , 'VISITS, , and SPEND. Which of the following steps, combined and executed in the correct order, would be the MOST efficient and secure way to automate this report delivery?

A. Create a Snowflake Alert that triggers when the 'WEB ACTIVITY table is updated. The alert executes a stored procedure that queries the data, formats it as CSV using Snowflake Scripting, writes the CSV to an internal stage, and then uses a Python UDF to send the email with the CSV attachment. Grant the alert necessary privileges.
B. Create a Snowflake Stream on the 'WEB ACTIVITY table. When data changes are detected, trigger an external function (e.g., AWS Lambda) via a pipe. The Lambda function queries the data, formats it as CSV, uploads the file to S3, and sends an email with a download link.
C. Use a third-party reporting tool (e.g., Tableau, Power Bl) to connect to Snowflake, create the report, and schedule it for daily email delivery with a CSV attachment. Configure the tool with the appropriate Snowflake connection details and credentials.
D. Create a scheduled task in an external orchestrator (e.g., Airflow). This task uses the Snowflake Python connector to query the data, format it as CSV, writes the CSV to an external stage (e.g., AWS S3), and then uses an email service (e.g., AWS SES) to send the email with the CSV attachment. Configure appropriate IAM roles for Snowflake to access S3.
E. Create a Snowflake Task that executes a stored procedure. The stored procedure uses a Snowflake Scripting block to query the data, format it as CSV using Javascript UDF, writes the CSV to an internal stage, and then uses a Java UDF (Util.EmailSender) to send the email with the CSV attachment. Grant necessary permissions to the task's service account to access the stage and execute the UDFs.

Answer: D

Explanation:
Option B is the most efficient and scalable. Using an external orchestrator provides better scheduling control and separation of concerns. Writing to S3 allows for easier integration with other external services. Python connector is preferred for data transformation and CSV generation compared to Snowflake scripting. Utilizing AWS SES for email delivery scales well and aligns with a cloud-native approach. Option A is viable but can be resource-intensive within Snowflake. Options C, D and E are less efficient due to relying on event-based triggers or third party tools for scheduling and email delivery. Option D exposes the data to a third-party tool. Option E requires complex setup and is not the best choice for a simple daily report.

NEW QUESTION # 22
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