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New Launch DEA-C02 PDF Dumps [2025] - Snowflake DEA-C02 Exam Question

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q197-Q202):

NEW QUESTION # 197
You are using the Snowflake Developer API to automate the creation and management of masking policies. You need to create a masking policy that masks an email address using SHA256 hashing. You also want to ensure that the policy can be applied to multiple tables and columns without modification. Assuming you have already established a connection to Snowflake using the Developer API, which of the following code snippets BEST demonstrates how to create and apply this masking policy using Python?

A. Option D
B. Option B
C. Option E
D. Option A
E. Option C

Answer: C

Explanation:
Option E is the best approach because: It uses 'CREATE OR REPLACE MASKING POLICY', which allows you to update the policy definition if it already exists. It defines the input parameter as 'ANY' and then casts it to 'VARCHAR within the SHA2 function. This makes the policy more versatile and applicable to columns with different data types (as long as they can be cast to VARCHAR). Using CREATE OR REPLACE MASKING POLICY ensure no error will be raised when the policy already exist. The 'val ANY part ensures that policy work for all type of data.

NEW QUESTION # 198
You have an external table in Snowflake pointing to data in Azure Blob Storage. The data consists of customer transactions, and new files are added to the Blob Storage daily You want to ensure that Snowflake automatically picks up these new files and reflects them in the external table without manual intervention. However, you are observing delays in Snowflake detecting the new files. What are the potential reasons for this delay and how can you troubleshoot them? (Choose two)

A. The external table's 'AUTO_REFRESH' parameter is set to 'FALSE', which disables automatic metadata refresh.
B. Snowflake's internal cache is not properly configured; increasing the cache size will solve the problem.
C. The file format used for the external table is incompatible with the data files in Blob Storage.
D. The storage integration associated with the external table does not have sufficient permissions to access the Blob Storage.
E. The Azure Event Grid notification integration is not properly configured to notify Snowflake about new file arrivals in the Blob Storage.

Answer: A,E

Explanation:
The two primary reasons for delays in Snowflake detecting new files in an external table are: 1) Incorrect configuration of the cloud provider's notification service (Azure Event Grid in this case). Snowflake relies on these notifications to be informed about new file arrivals. If the integration isn't set up correctly, Snowflake won't know when to refresh the metadata. 2) The parameter must be set to ' TRUE' for automatic metadata refresh to occur. If it's set to FALSE , manual refreshes are required using 'ALTER EXTERNAL TABLE ... REFRESH". Options D and E, although possible issues, won't directly cause a delay in detecting new files, but rather cause issues accessing files after detection. Option C is irrelevant as Snowflake's caching mechanism does not directly impact external table metadata refresh.

NEW QUESTION # 199
You are creating a Snowflake Listing to share data with multiple consumers. One consumer requires access to the complete dataset while other consumers need access to a subset of the data based on geographical region (e.g., only data related to the 'US'). You want to minimize data duplication and management overhead. Select all the valid ways to implement this using Snowflake Data Sharing features.

A. Share the base tables in the Listing and instruct each consumer to filter the data based on their region using a WHERE clause.
B. Create multiple Listings, one for each region and one for the complete dataset.
C. Share the base tables in the Listing and use Row Access Policies to filter data based on region for specific consumers and allow full access to the entire dataset for the consumer requiring full access. Monitor usage through Snowflake's account usage views.
D. Create multiple secure views, each filtered by region, and create a single Listing that shares all views. Grant access to the appropriate view based on the consumer's region.

Answer: C

Explanation:
Row Access Policies is the most efficient method because it enables fine-grained access control on the underlying data without creating separate datasets. Option A requires data duplication. Option B is less secure because it relies on the consumer to implement the filtering. Option D creates an overhead by creating multiple Views, and is hard to maintain with changing consumers. Option E hides the data access using a stored procedure, reducing the benefits of data sharing through direct access.

NEW QUESTION # 200
A healthcare provider wants to share patient data with a research organization, but must ensure that researchers only have access to records from a specific region ('REGION A') and only see anonymized data'. You have a 'patients' table with columns 'patient_id' , 'region', 'dob', 'medical history', and 'ssn'. Which of the following steps would be MOST effective and secure for implementing row- level filtering and data masking for this data sharing scenario, minimizing administrative overhead and maximizing query performance?

A. Create a stored procedure that filters the 'patients' table based on the region and applies masking policies. The stored procedure writes the filtered and masked data to a new table. Grant access to the new table to the research organization's role.
B. Create a VIEW on the 'patients' table with a WHERE clause filtering by 'region = and use masking policies to redact sensitive fields. Grant access to the view to the research organization's role.
C. Create a secure VIEW on the 'patients' table with a WHERE clause filtering by 'region = 'REGION_A" and use masking policies to redact sensitive fields. Grant access to the secure view to the research organization's role.
D. Create a policy tag on the 'region' column restricting access to 'REGION_A' and apply masking policies directly on the 'patients' table. Grant access to the patientS table to the research organization's role.
E. Create a standard VIEW on the 'patients' table with a WHERE clause filtering by 'region = 'REGION_A". Create a separate table containing only the anonymized data for 'REGION_A' and grant access to this separate table to the research organization's role.

Answer: C

Explanation:
Using a secure VIEW is the most efficient and secure method. It provides row-level filtering (REGION_A) and masking policies, while secure views prevent access to the underlying table's data lineage even with ACCOUNTADMIN privileges. This is preferable to standard views because standard views can expose the underlying table structure. Stored procedures add unnecessary complexity, a separate table duplicates data, and policy tags require governance roles and don't, on their own, handle masking requirements. Also, creating a separate table results in duplicated data and is not efficient and secure and adds administrative overhead.

NEW QUESTION # 201
A company stores raw clickstream data in AWS S3. They need to query this data occasionally (less than once per day) for ad-hoc analysis and auditing purposes without ingesting it into Snowflake. Which of the following approaches is MOST suitable and cost- effective, and which considerations regarding data freshness are crucial?

A. Create an internal stage in Snowflake and regularly copy the data from S3 into the internal stage using a Snowpipe. Query the data in the internal stage. Data freshness depends on the Snowpipe configuration.
B. Create an external table pointing directly to the S3 bucket. The data freshness depends on the automatic refresh enabled using the object. Ensure a metadata notification service (e.g., SQS) is configured to trigger the metadata refresh whenever new files are added to S3.
C. Create an external table pointing directly to the S3 bucket. The data freshness depends on manually refreshing the metadata using 'ALTER EXTERNAL TABLE ... REFRESH'. Automate this refresh with a scheduled task.
D. Load the data into a Snowflake table using COPY INTO and schedule a daily task to refresh the table. Data freshness is guaranteed by the scheduled task.
E. Create an external table pointing directly to the S3 bucket. Data freshness is automatically maintained by Snowflake whenever the external table is queried. No additional configuration is required.

Answer: B

Explanation:
External tables are ideal for infrequent queries of data residing in external storage. The 'EVENT_TABLE object provides an automatic refresh, but requires the setup of a notification service to trigger the refresh when new files are written to S3. Manually refreshing the metadata with "ALTER EXTERNAL TABLE ... REFRESH' is also a valid approach, but requires manual intervention or scheduling. Data freshness is NOT automatically maintained; a metadata refresh is REQUIRED.

NEW QUESTION # 202
......

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