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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q301-Q306):

NEW QUESTION # 301
You have developed a Snowsight dashboard for your marketing team that contains sensitive customer data'. You need to share this dashboard with a specific group of users, but ensure that they can only view the data and cannot modify the dashboard itself or the underlying queries. Which of the following steps should you take to securely share the dashboard?

A. Share the dashboard with the group using the 'Can edit' permission. Then, grant the users in the group the 'USAGE' privilege on the database and schema containing the data.
B. Share the dashboard with the group using the 'Can view' permission. Ensure the group has the 'SELECT' privilege on the tables/views used in the queries and the ' USAGE privilege on the database and schema. Also ensure that any intermediate tables created by the dashboard are also granted these privleges.
C. Share the dashboard with 'Can view' permission and no further action is required.
D. Share the dashboard with the group using the 'Can view' permission. Then, grant the users in the group the 'MONITOR privilege on the virtual warehouse used by Snowsight.
E. Create a scheduled task to export the dashboard as a PDF and email it to the group on a daily basis.

Answer: B

Explanation:
Sharing with 'Can view' (B) ensures the users cannot modify the dashboard itself. Granting 'SELECT on the underlying tables/views and USAGE' on the database/schema allows them to see the data when the dashboard executes queries on their behalf. 'Can edit' (A) would allow them to modify the dashboard, which is not desired. 'MONITOR (C) is for monitoring warehouse usage, not data access. Exporting as PDF (D) is a workaround, but it loses interactivity and isn't a direct sharing method. E isn't correct because permissions on the data sources must be granted separately.

NEW QUESTION # 302
You are using Snowpipe to continuously load data from an external stage (AWS S3) into a Snowflake table named 'RAW DATA. You notice that the pipe is frequently encountering errors due to invalid data formats in the incoming files. You need to implement a robust error handling mechanism that captures the problematic records for further analysis without halting the pipe's operation. Which of the following approaches is the MOST effective and Snowflake-recommended method to achieve this?

A. Disable the Snowpipe and manually load data using a COPY INTO statement with the 'ON_ERROR = 'SKIP_FILE" option, then manually inspect the skipped files.
B. Utilize Snowpipe's 'VALIDATION_MODE' parameter set to to identify and handle invalid records. This requires modification of the COPY INTO statement to redirect errors to an error table.
C. Implement Snowpipe's 'ERROR _ INTEGRATION' object, configuring it to automatically log error records to a designated stage location in JSON format for later analysis. This requires updating the pipe definition.
D. Configure Snowpipe's 'ON_ERROR parameter to 'CONTINUE' and rely on the 'SYSTEM$PIPE_STATUS' function to identify files with errors. Then, manually query those files for problematic records.
E. Implement a custom error logging table and modify the Snowpipe's COPY INTO statement to insert error records into this table using a stored procedure called upon failure.

Answer: C

Explanation:
Snowflake's 'ERROR INTEGRATION' feature, when configured with a pipe, automatically logs details of records that fail during ingestion to a specified stage. This provides a structured and readily accessible log of errors without interrupting the data loading process. Option A is not a native feature. Option B, while potentially usable, doesn't directly integrate with pipes as the PRIMARY mechanism. Option C involves more manual intervention and doesn't offer structured error logging. Option E defeats the purpose of automated loading via Snowpipe.

NEW QUESTION # 303
You are tasked with designing a Snowflake data model for a system that tracks changes to product information. You need to store both the current and historical states of the 'PRODUCTS' table. Which of the following strategies, regarding primary keys and table structures, is the MOST appropriate for efficiently querying the current product state and historical changes, considering best practices for Snowflake and minimizing storage costs? Select all that apply.

A. Create a single table, 'PRODUCTS', with columns 'PRODUCT_ID (primary key), 'PRODUCT NAME, 'PRODUCT DESCRIPTION', 'EFFECTIVE DATE and 'END_DATE. Implement clustering on 'PRODUCT_ID' and 'EFFECTIVE_DATE'.
B. Create two tables: with 'PRODUCT_ID as the primary key, storing the current product information, and 'PRODUCTS_HISTORY' with 'PRODUCT ID', 'EFFECTIVE DATE, and 'END DATES columns, where the combination of 'PRODUCT and 'EFFECTIVE DATE' serves as a composite key. Use a scheduled task to copy data from 'PRODUCTS_CURRENT to 'PRODUCTS_HISTORY whenever a product is updated.
C. Create a single table, 'PRODUCTS', with columns 'PRODUCT_ID (primary key), 'PRODUCT_NAME', 'PRODUCT_DESCRIPTION', 'EFFECTIVE_DATE', and 'END_DATE. Whenever a product attribute changes, insert a new row with the updated attribute values and the new 'EFFECTIVE_DATE , setting the 'END_DATE' of the previous record to the ' EFFECTIVE_DATE of the new record.
D. Create a single table, 'PRODUCTS', with 'PRODUCT_ID as the primary key and a 'JSON' column to store the historical product data. Use a stored procedure to update the ' JSON' column whenever a product attribute changes.
E. Create a single table, 'PRODUCTS', with columns 'PRODUCT ID, 'PRODUCT NAME', 'PRODUCT DESCRIPTION', 'EFFECTIVE DATE, and 'END_DAT Add a sequence to act as a surrogate key and define this sequence as the primary key.

Answer: A,C

Explanation:
Option B correctly describes a type 2 slowly changing dimension (SCD2) approach. This is a common and effective way to track historical changes. Each row represents a specific state of a product over a defined period (between 'EFFECTIVE_DATE and 'END_DATE). While Snowflake doesn't enforce primary keys, defining 'PRODUCT ID' as the primary key provides metadata to the optimizer. Option D suggests clustering on "PRODUCT_ID and which is crucial for optimizing queries that retrieve product information for specific time periods. This clustering will significantly improve query performance. Option A is less efficient, the scheduled task copies data which leads to higher compute cost. JSON isn't scalable since querying JSON efficiently requires specific functions. Option E introduces surrogate keys that can be useful, clustering helps more here.

NEW QUESTION # 304
You are responsible for a Snowflake data pipeline that loads data from various external sources into a data warehouse. You have implemented Snowpipe for continuous data ingestion from AWS S3. One of your data sources is known to occasionally produce corrupted data files. You need to design a resilient pipeline that can handle these corrupted files without halting the entire data loading process and allows for the identification and quarantine of these files for further investigation. Assume you have a table called 'RAW DATA' and you are using a file format 'MY CSV FORMAT. Select TWO actions which best address this situation:

A.
B. Configure Snowpipe's 'ERROR INTEGRATION' to log details of files that fail during ingestion, including the error messages. Then, set ERROR-CONTINUE" on the Copy Statement. This requires an external stage for the error logs. In addition, create a separate task that queries the ERROR INTEGRATION external stage and moves the corrupt files into a separate quarantine bucket.
C. Implement a pre-processing step using a Snowflake task to validate the data in each file before it's loaded into the 'RAW_DATR table using a 'COPY INTO' statement. The Task would then move files failing validation to a quarantine bucket.
D. Set 'ON_ERROR = 'SKIP_FILE" in the 'COPY INTO' statement used by Snowpipe. This setting will skip entire files that contain errors, preventing them from being loaded into the table.
E. Create a separate stream on the 'RAW DATA' table that filters for records with errors and then move these erroneous records into a quarantine table using a task. Implement ERROR=CONTINIJE on the Copy Statement.

Answer: B,C

Explanation:
Option B directly addresses the requirement for a resilient pipeline by utilizing 'ERROR INTEGRATION' to log errors and quarantine corrupt files. Setting ON_ERROR to CONTINUE avoids pipeline interruption, and creating a task to query the error stage and move files to a quarantine bucket achieves the desired outcome. Option C would be the 2nd best option because it provides for pre-processing and validation, which avoids loading the corrupted records into the RAW_DATA table in the first place. Option A only skips the file and doesn't log the errors or quarantine the file. Option D creates a stream on the RAW DATA table, but we would like to avoid writing erroneous records to the RAW DATA table in the first place. 'VALIDATE only checks the existing data, so it would not solve any problems in Data Load.

NEW QUESTION # 305
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 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.
B. 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.
C. 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.
D. 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.
E. 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.

Answer: A

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 # 306
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Prepare with Snowflake DAA-C01 PDF Questions [2025]-Best Preparation Materials

Reliable DAA-C01 Exam Testking, Top DAA-C01 Questions

Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q301-Q306):

Free PDF Quiz Snowflake - DAA-C01 Useful Exam Lab Questions