DAA-C01 Valid Test Sims, DAA-C01 Actual Exam

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Build and Troubleshoot Advanced SQL Queries	20–25%	- Semi-structured data processing - Complex joins, subqueries, window functions - Query optimization and troubleshooting
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%	- Views, materialized views, CTEs - Handling NULLs and structuring datasets - Data cleansing, standardization, type conversion
Prepare and Load Data	15–20%	- External tables and data validation - Data ingestion methods: COPY INTO, stages, Snowpipe - File formats: CSV, JSON, Parquet, Avro
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%	- Using Snowflake ML and built-in analytics - Forecasting and predictive modeling
Perform Descriptive and Diagnostic Analysis	10–15%	- Statistical summarization and trend analysis - Exploratory and ad-hoc analysis - Anomaly detection and root cause analysis

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Free PDF 2026 DAA-C01: SnowPro Advanced: Data Analyst Certification Exam High Hit-Rate Valid Test Sims

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
A retail company wants to build a dashboard to track sales performance by region. They have implemented Dynamic Data Masking on the 'CUSTOMER PHONE' column in their 'CUSTOMERS' table. A user with the 'ANALYST ROLE' needs to see the sales data and masked phone numbers in the dashboard. However, the company also has a Row Access Policy applied to the 'SALES' table that restricts access based on the 'REGION' column, only allowing users to see data from their assigned region. The 'ANALYST ROLE is NOT assigned any specific region. Which approach will allow the dashboard to display the sales data and masked phone numbers for all regions without violating security policies?

A. Create a view that joins the 'SALES' and 'CUSTOMERS' tables. Grant the 'ANALYST_ROLES 'SELECT privilege on the view. Remove the Row Access Policy from the 'SALES' table. The dynamic masking policy will still be applied.
B. Create a secure view that joins the 'SALES' and 'CUSTOMERS' tables, granting the 'ANALYST_ROLE 'SELECT privilege on the view. The secure view preserves the Row Access Policy and dynamic masking, ensuring data security and role-based access.
C. Create a stored procedure that executes with 'OWNER rights. Inside the stored procedure, query the 'SALES' and 'CUSTOMERS' tables. Grant the 'ANALYST ROLE execute privilege on the stored procedure. This bypasses both the Row Access Policy and dynamic data masking and displays all data without any restrictions.
D. Create a stored procedure that executes with 'CALLER rights. Inside the stored procedure, query the 'SALES and 'CUSTOMERS' tables. Grant the execute privilege on the stored procedure. This bypasses the Row Access Policy and displays all data while still applying dynamic data masking.
E. Create a view that joins the 'SALES' and 'CUSTOMERS' tables, granting the 'ANALYST_ROLE 'SELECT privilege on the view. The view will inherit the Row Access Policy of the 'SALES' table, preventing the analyst from seeing all regions. The dynamic masking policy on 'CUSTOMER PHONE will still be applied.

Answer: B

Explanation:
A secure view is the best solution. It encapsulates the data access logic and respects both the Row Access Policy (limiting regional access) and Dynamic Data Masking (masking sensitive phone number data). Option B bypasses Row Access policies which isn't desirable. Option D is wrong as removing the Row Access Policy is generally a bad security practice. Option E bypasses the Row Access Policy and masking which is also undesirable.

NEW QUESTION # 21
Your organization is migrating a large on-premise data warehouse (100 TB) to Snowflake. The existing data warehouse contains complex data transformations implemented in stored procedures. During the migration, you need to minimize downtime and accurately estimate the data volume transferred to Snowflake. You decide to use a hybrid approach with both batch and incremental loading. Which of the following strategies would be MOST appropriate?

A. Perform a full batch load of the historical data using Snowpipe from a cloud storage location. After the initial load, implement a change data capture (CDC) solution using a third-party tool (e.g., Debezium, Qlik Replicate) to incrementally load changes into Snowflake.
B. Create external tables pointing to the on-premise data warehouse. Run queries directly against the external tables to transform and load the data into Snowflake. Once all data is loaded, drop the external tables. Use 'TABLE_SIZE function for sizing calculation.
C. Implement a custom ETL pipeline using Apache Spark to extract data from the on-premise data warehouse, perform transformations, and load the data into Snowflake using the Snowflake JDBC driver. Continuously run Spark jobs to keep the data synchronized.
D. Utilize Snowflake Connectors (e.g., Kafka connector) to stream data directly from the on-premise data warehouse to Snowflake. Implement a data replication tool to handle the initial data load and ongoing synchronization.
E. Use Snowflake's Data Exchange to directly replicate the on-premise data warehouse to Snowflake. Rely on Data Exchange's built-in monitoring to track the data volume transferred.

Answer: A

Explanation:
Option B is the most appropriate strategy. It allows for a faster initial load using Snowpipe, leveraging the scalability of cloud storage. Implementing a CDC solution minimizes downtime and ensures data synchronization after the initial load. The combined approach addresses both the large data volume and the need for continuous updates.

NEW QUESTION # 22
Which statement accurately describes the use of regular views in data analysis?

A. They simplify complex data structures, aiding ease of analysis.
B. Regular views limit data accessibility for improved security.
C. Regular views are exclusively used for administrative tasks.
D. Regular views offer real-time updates to reflect instantaneous database changes.

Answer: A

Explanation:
Regular views aid analysis by simplifying complex data structures, improving comprehension.

NEW QUESTION # 23
How can automated and repeatable tasks contribute to maintaining reports and dashboards to meet business requirements?

A. They solely increase the complexity of dashboard management.
B. They limit the scalability of dashboards and reports.
C. Repeatable tasks hinder data updates in dashboards.
D. Automated tasks ensure consistency and reduce manual effort.

Answer: D

Explanation:
Automated tasks ensure consistency and reduce manual effort in maintaining reports and dashboards.

NEW QUESTION # 24
You are loading data into a Snowflake table 'PRODUCT PRICES' using the COPY INTO command from a Parquet file stored in Azure Blob Storage. The 'PRODUCT PRICES' table has the following schema: VARCHAR, PRICE CURRENCY VARCHAR, LAST UPDATED TIMESTAMP NTZ. The Parquet file contains all these columns, but the 'LAST UPDATED column is stored as a Unix epoch timestamp (seconds since 1970-01-01 00:00:00 UTC). You need to transform the Unix epoch timestamp to a Snowflake TIMESTAMP NTZ during the data load. Which of the following options correctly demonstrates how to achieve this using a transformation within the COPY INTO command?

Answer: D

Explanation:
The correct answer is C. When using transformations with COPY INTO and an external stage, you must explicitly select the columns using '$1', '$2, etc. The gazure_stage/data.parquet' path is used in the 'FROM' clause, and converts the fourth column (LAST_UPDATED) from the Parquet file to a Snowflake TIMESTAMP. Note: It assumes column ordering in the Parquet file matches the order in the select statement. Using a SELECT statement within the FROM' clause is the correct approach when applying transformations directly during the COPY. The other options are incorrect because they either use the deprecated 'TRANSFORMATION' parameter (now replaced by select statements in the FROM clause) or attempt to apply transformations incorrectly. This is the only answer that is correct and will work. Options A, B, D, and E will all fail during the COPY INTO statement.

NEW QUESTION # 25
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