Free PDF 2025 Professional DAA-C01: SnowPro Advanced: Data Analyst Certification Exam Exam Quiz

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
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. Create a scheduled task to export the dashboard as a PDF and email it to the group on a daily basis.
B. Share the dashboard with 'Can view' permission and no further action is required.
C. 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.
D. 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.
E. 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.

Answer: E

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 # 33
In data presentations for business use analyses, why is identifying patterns and trends crucial?

A. Identifying patterns and trends aids in insightful analyses.
B. Patterns and trends have minimal impact on business use analyses.
C. It complicates data analysis, hindering decision-making.
D. Recognizing patterns and trends restricts data exploration.

Answer: A

Explanation:
Identifying patterns and trends aids in insightful analyses in business use scenarios.

NEW QUESTION # 34
You are tasked with analyzing website traffic data stored in a Snowflake table named 'page_views'. The table has columns 'user_id' (INT), 'page_url (VARCHAR), and 'view_time' (TIMESTAMP N T Z). You need to identify users who are likely bots based on an unusually high number of page views within a short period. Specifically, you want to flag users who have more than 100 page views within any 5-minute window Which of the following queries is the MOST efficient and accurate way to achieve this?

Answer: C

Explanation:
Option C is the most accurate and efficient. It correctly calculates the number of views within a 5-minute window for each user using the 'DATE DIFF function and a window function. It then filters to only include users who exceed 100 views in any of those windows, and ensures each user is only counted once with the DISTINCT keyword. Option A is incorrect because it only looks at total page views across the entire dataset. Option B is syntactically incorrect and doesn't implement the time window. Option D is truncating by minute and then groups across that resulting in incorrect aggregation. The correct function is 'DATE DIFF, not 'DATE TRUNC and the logic for window is incorrect. Option E will result in errors as TUMBLE_START requires view_time to be timestamp_ltz or timestamp_tz data type.

NEW QUESTION # 35
When creating reports and dashboards, how does evaluating data based on business requirements impact the visualization process?

A. Evaluating data complicates dashboard creation.
B. Evaluating data ensures relevant and useful dashboard content.
C. Business requirements have no impact on data selection for visualization.
D. It limits data selection, affecting overall dashboard quality.

Answer: B

Explanation:
Evaluating data based on business requirements ensures the dashboard contains relevant and useful content, improving its quality.

NEW QUESTION # 36
You have a Snowpipe configured to load CSV files from an AWS S3 bucket into a Snowflake table. The CSV files are compressed using GZIP. You've noticed that Snowpipe is occasionally failing with the error 'Incorrect number of columns in file'. This issue is intermittent and affects different files. Your team has confirmed that the source data schema should be consistent. What combination of actions provides the most likely and efficient solution to address this intermittent column count mismatch issue?

A. Adjust the parameter in the file format to FALSE. This will allow Snowpipe to load the data, skipping rows with incorrect column counts. Implement a separate process to identify and handle skipped rows.
B. Set the 'SKIP_HEADER parameter in the file format to 1 and ensure that a header row is consistently present in all CSV files. Also implement a task that validates that the header of all CSV files are correct.
C. Recreate the Snowflake table with a 'VARIANT column to store the entire CSV row as a single field. Then, use SQL to parse the 'VARIANT* data into the desired columns.
D. Investigate the compression level of the GZIP files. Some compression levels might lead to data corruption during decompression, causing incorrect column counts. Lowering the compression might help.
E. Check for carriage return characters within the CSV data fields. These characters can be misinterpreted as row delimiters, leading to incorrect column counts. Use the and 'RECORD_DELIMITER parameters in the file format to correctly parse the CSV data.

Answer: A,E

Explanation:
Setting *ERROR ON COLUMN COUNT MISMATCH' to FALSE allows the pipe to continue without halting on such errors. However, this approach will leave behind bad records. Carriage return issues can occur, which affect the column count when ingesting data. If there are carriage return characters inside the CSV fields, this will be misinterpreted as delimiters. Option A might help if headers are present and consistent, but is less likely the root cause of an intermittent column count mismatch. Option C is unlikely to be a primary cause of column count issues as GZIP decompression is generally reliable. Option E is a workaround, but less efficient than correctly configuring the CSV parsing.

NEW QUESTION # 37
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Free PDF 2025 Professional DAA-C01: SnowPro Advanced: Data Analyst Certification Exam Exam Quiz

DAA-C01 Valid Test Book & DAA-C01 Latest Practice Materials

Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q32-Q37):

100% Pass Quiz 2025 Realistic Snowflake DAA-C01 Exam Quiz

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