Snowflake DAA-C01 Questions Latest DAA-C01 Dumps PDF [2026]

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Objectives
Data Loading and Unloading	- Data ingestion methods 1. COPY INTO and bulk loading 2. Continuous ingestion and Snowpipe concepts - Data export 1. UNLOAD and external stages
Data Modeling and Performance Optimization	- Modeling approaches in Snowflake 1. Star and snowflake schemas 2. Data normalization vs denormalization - Performance tuning 1. Clustering and pruning techniques 2. Warehouse sizing and auto-suspend/auto-resume
Security, Governance, and Data Sharing	- Data sharing and governance 1. Secure data sharing 2. Data masking and policies - Access control and security 1. Authentication and encryption concepts 2. Role-based access control (RBAC)
Snowflake Architecture and Data Platform Fundamentals	- Snowflake architecture concepts 1. Virtual warehouses and scaling 2. Cloud services layer, compute layer, storage layer - Data platform fundamentals 1. Data lifecycle in Snowflake 2. Separation of storage and compute
Data Transformation and Analysis	- Analytical workloads 1. Query optimization for analytics 2. Materialized views and caching - SQL-based transformations 1. Semi-structured data (VARIANT, JSON, XML) 2. Joins, aggregations, window functions

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

NEW QUESTION # 52
When managing Snowsight dashboards, what role do subscriptions and updates play in meeting business requirements?

A. Subscriptions and updates ensure timely information delivery.
B. Managing subscriptions and updates complicates dashboard usage.
C. They enhance dashboard usability without impacting data updates.
D. Subscriptions and updates don't impact dashboard management.

Answer: A

Explanation:
Subscriptions and updates ensure timely information delivery, meeting business requirements.

NEW QUESTION # 53
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. 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'.
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. Use Snowpipe with a file format object specifying 'TYPE = JSON' and 'COMPRESSION = GZIP'. Configure error notifications for the pipe and handle errors manually.
D. 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.
E. 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.

Answer: A

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 # 54
When managing Snowsight dashboards, what role do subscriptions and updates play in meeting business requirements?

A. Subscriptions and updates ensure timely information delivery.
B. Managing subscriptions and updates complicates dashboard usage.
C. They enhance dashboard usability without impacting data updates.
D. Subscriptions and updates don't impact dashboard management.

Answer: A

Explanation:
Subscriptions and updates ensure timely information delivery, meeting business requirements.

NEW QUESTION # 55
What is a benefit of using SQL queries that contain secure views?

A. Only the number of scanned micro-partitions is exposed, not the number of bytes scanned.
B. Users will not be able to make observations about the quantity of underlying data.
C. The amount of data scanned, and the total data volume are obfuscated.
D. Snowflake secure views are more performant than regular views.

Answer: B

Explanation:
Secure Views in Snowflake are specifically designed for data privacy and security, particularly when data is being shared across different business units or external accounts. The primary benefit of a secure view is that it prevents users from seeing the internal definitions (the underlying SQL) and protects against "trial-and- error" data discovery.
In a standard view, a savvy user might be able to deduce information about data they are not authorized to see by observing the query optimizer's behavior or by looking at the query profile. For example, by using specific filters or functions, a user might observe how long a query takes to execute or check the statistics to guess the distribution of values in hidden columns. Secure views prevent this by ensuring that Snowflake does not expose the internal metadata or the specific row/column counts that would allow a user to make observations about the quantity or nature of the underlying data that falls outside their access privileges.
Evaluating the Options:
* Option B and C are incorrect because Snowflake does not merely obfuscate bytes or micro-partitions in a way that suggests a simple "hiding" of metrics; it fundamentally changes the query plan generation to ensure security.
* Option D is a common misconception. In fact, secure views can sometimes be less performant than regular views. This is because the Snowflake optimizer is restricted from using certain optimizations (like predicate pushdown) that might inadvertently reveal data patterns to an unauthorized user.
* Option A is the 100% correct answer. The "secure" nature of the view ensures that the user cannot use statistical side-channels or metadata observations to infer the existence or volume of data they are restricted from seeing.

NEW QUESTION # 56
You are building a Snowsight dashboard that visualizes website traffic data'. The data includes a column 'visit_timestamp' (TIMESTAMP NTZ) and you need to display the number of unique visitors per hour for the last 24 hours. You also want to allow users to filter the data by country. You plan to use a chart to visualize the trend. Which of the following approaches are the MOST efficient and accurate for achieving this?

A. Create a calculated field in Snowsight that extracts the hour from the 'visit_timestamp' and then group by that calculated field to calculate the distinct count of visitor IDs. Apply the country filter as a dashboard-level filter in Snowsight.
B. Create a view that pre-aggregates the hourly visitor counts and includes the country. The dashboard queries this view and applies no additional filters.
C. Use the function in the SQL query to group the data by hour and calculate the distinct count of visitor IDs. Apply the country filter as a dashboard-level filter in Snowsight.
D. Use the visit_timestamp)' function in the SQL query to group the data by hour and calculate the distinct count of visitor IDs. Apply the country filter directly in the SQL query.
E. Use the 'TO_CHAR(visit_timestamp, 'YYYY-MM-DD HH24')' function to group data by hour, but only use this option if 'visit_timestamp' is already stored as TEXT.

Answer: B,D

Explanation:
Option A is a good approach because 'DATE _ TRUNC is efficient for truncating timestamps, and applying the filter directly in the SQL query can optimize data retrieval. Option D is also very efficient, as it pre-aggregates the data, which improves dashboard performance. Option B is less efficient because the 'HOUR' function only returns the hour value without the date, making it harder to filter for last 24 hours. Also, dashboard-level filters can sometimes be less performant than SQL-level filters for large datasets. Option C introduces a calculated field, which is generally less efficient than performing the transformation directly in SQL. Option E is technically correct, it only applies to TEXT stored visit timestamp. Thus, pre-aggregation and 'DATE_TRUNC' are superior.

NEW QUESTION # 57
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