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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
You have Parquet files containing customer order data in an external stage. You need to create a secure view in Snowflake that filters the data based on user roles. Only users with the 'SALES REP' role should see orders from their assigned region, while 'MANAGER' roles should see all data'. Assuming you have set up appropriate role hierarchies, how would you implement this using row-level security policies with Parquet external tables?

A. Create a secure view that queries the external table. Then, define a row access policy that filters the data based on the user role and the region, and apply the policy to the secure view. The policy should use to determine the appropriate filtering criteria. Delegate the ownership to 'ACCOUNTADMIN'.
B. Create an external table with a schema defined according to the Parquet files. Define a masking policy that hides the sensitive information for users without proper roles. Create a standard view on top of the external table. Apply the masking policy to a column inside view.
C. Create a standard view querying the external table. Create a row access policy that filters the data based on the user role and the region. Apply the policy to the external table. Delegate the ownership to 'ACCOUNTADMIN'.
D. Create a secure view that directly queries the Parquet files using to access the region information. Apply a WHERE clause in the view definition using to filter based on the user's role. No RLS policy needed.
E. Create a secure view that queries the Parquet files directly using 'JSON EXTRACT PATH TEXT to access the region information. Apply a JOIN operation with a role table to filter the data based on the user's role.

Answer: A

Explanation:
Row Access Policies (RAP) are designed for Row Level Security, and they are applied to tables or views. Since the data is in an external table accessed through a view, the policy should be attached to the secure view. The ' function is used to determine the user's current role within the policy's logic. Secure Views protect the underlying data and policy logic from unauthorized access. Delegating ownership to 'ACCOUNTADMIW is not strictly required but a good security practice. Masking policies aren't the correct choice here as it is used to hide data from the user.

NEW QUESTION # 20
You are using Snowpipe to continuously load JSON data from an external stage. Occasionally, some JSON records are malformed and cause the pipe to fail. You want to configure the pipe to skip these invalid records and continue loading valid data, while also capturing the error details for later analysis. Which approach provides the most efficient and appropriate solution for this scenario?

A. Configure the Snowpipe definition to use the 'VALIDATE(O)' function within the 'COPY INTO' statement.
B. Use the 'ON ERROR = 'CONTINUE'' option in the 'COPY INTO' statement used by the Snowpipe definition in conjunction with the 'VALIDATE function to capture error details.
C. Use the 'VALIDATION MODE = RETURN ALL ERRORS parameter in the 'COPY INTO' statement and then filter the data based on the errors returned.
D. Implement custom error handling in your application code to pre-validate JSON records before uploading them to the stage.
E. Use the = 'SKIP_FILE" option in the 'COPY INTO' statement used by the Snowpipe definition.

Answer: B

Explanation:
Option E is the most efficient and complete solution. SON ERROR = 'CONTINUE" allows Snowpipe to skip bad records and continue processing. Using it in conjunction with the 'VALIDATE function within the 'COPY INTO' statement enables capturing error information for analysis. This combines error skipping with error logging. Options A, B, C, and D are either less efficient (requiring pre-processing or post- processing of data), or do not provide a comprehensive solution for both skipping and capturing error information. Using (option B) is too coarse-grained as it skips entire files, even with only a few errors. Using 'VALIDATION MODE without ERROR=CONTINUE will still stop the pipe on errors.

NEW QUESTION # 21
You have a Snowflake table 'EMPLOYEES' with columns 'EMPLOYEE ID' ONT, PRIMARY KEY), 'SALARY' and 'DEPARTMENT' (VARCHAR). You need to enforce the following business rules: 1. 'SALARY' must be a positive value. 2. 'DEPARTMENT' must be one of the following values: 'SALES', 'MARKETING', 'ENGINEERING'. 3. If the Employee is in 'SALES' Department, Salary should be between 50000 and 100000. Which of the following is the most appropriate and efficient approach using Snowflake constraints and features?

A. Enforce rules using stored procedure at the time of insertion and updation.
B. Use a CHECK constraint for 'SALARY > , an ENUM type for 'DEPARTMENT' , and a TRIGGER to enforce the salary range rule for the 'SALES' department.
C. Use a CHECK constraint for 'SALARY > , a CHECK constraint 'DEPARTMENT IN ('SALES', 'MARKETING', 'ENGINEERING') , and a third CHECK constraint 'CASE WHEN DEPARTMENT = 'SALES' THEN SALARY BETWEEN 50000 AND 100000 ELSE TRUE END'.
D. Use CHECK constraints for both rules and a third CHECK constraint to combine rules 2 and 3 to apply on each record.
E. Use CHECK constraint for SALARY, Create a Lookup table for Departments, and apply a Foreign key relationship for DEPARTMENT field in EMPLOYEE table.

Answer: C

Explanation:
Option D is the most appropriate and efficient. CHECK constraints are designed for these types of validations. The 'CASE' statement within the third CHECK constraint allows conditional validation based on the 'DEPARTMENT value. CHECK constriants are enforced at the time of record insert or update. Stored procedures could be an option but are not the most appropriate. Snowflake does not directly support ENUM types for column definitions. Creating Lookup table with Foreign key is another option.

NEW QUESTION # 22
A financial institution needs to collect stock ticker data for intraday trading analysis. The data source provides updates every second. They need to maintain a 5-minute rolling average of stock prices for each ticker. The system needs to be highly available and resilient to data source interruptions. Considering the need for near real-time analysis and potential data source instability, which combination of technologies and approaches would be MOST effective?

A. Leveraging Snowflake's dynamic data masking and data classification capabilities to maintain data security and compliance while adhering to real-time data ingestion.
B. Using a traditional ETL tool to extract, transform (calculate rolling average), and load the data into Snowflake in 15-minute intervals.
C. Employing a stream processing framework (e.g., Apache Kafka) to ingest the data, perform the rolling average calculation using a tumbling window, and load the aggregated results into Snowflake.
D. Using a scheduled task to query the API every minute and store the data directly into a Snowflake table with a materialized view calculating the rolling average.
E. Storing the raw data into Snowflake using Snowpipe in micro-batches and creating a VIEW that performs the rolling average calculation on-demand.

Answer: C

Explanation:
A stream processing framework like Kafka is ideal for handling high-velocity data streams. Kafka provides fault tolerance and the ability to perform real-time aggregations (rolling average with tumbling window). While Snowpipe can ingest the raw data quickly, calculating the rolling average on-demand (using a VIEW) may not meet the near real-time requirement and can be inefficient. A scheduled task might not be able to handle the volume and frequency of data. The key to answering this question is understanding the need for real-time aggregation AND resilience to potential data source outages, both of which Kafka elegantly addresses.

NEW QUESTION # 23
You are tasked with ingesting clickstream data from a website into Snowflake for real-time analytics. The website generates approximately 100,000 events per minute. The business requires insights into user behavior with a maximum latency of 5 minutes. Which data collection strategy would be MOST appropriate to meet these requirements, considering both cost and near real-time needs?

A. Near real-time ingestion using Snowpipe with auto-ingest enabled and micro-batches of data.
B. Directly inserting data using JDBC from the web application.
C. Utilizing a third-party ETL tool to transfer data in hourly batches.
D. Batch ingestion using Snowpipe with file sizes of IGB uploaded every 15 minutes.
E. Scheduled task using a Python script to extract data from an API endpoint every 10 minutes.

Answer: A

Explanation:
Near real-time ingestion using Snowpipe with auto-ingest is the most appropriate choice. It provides low latency (minutes), scales well with high data volume, and is cost-effective compared to maintaining a custom solution or paying for a full-fledged ETL tool for this specific scenario. Batch processing introduces too much latency (15 minutes or more), scheduled tasks can become resource intensive and JDBC inserts directly from the application can create performance bottlenecks and security concerns.

NEW QUESTION # 24
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