DAA-C01 Latest Exam, DAA-C01 Test Duration

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Snowflake DAA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Ingestion and Data Preparation	15%-20%	- Use best practice considerations relating to data integrity structures 1. Implement constraints 2. Perform table joins between parent/child tables 3. Define primary keys for tables - Perform data discovery to identify what is needed from available datasets 1. Determine the level of data granularity required 2. Evaluate required transformations (table joins, set operations, ASOF JOINS) 3. Query tables to assess data elements and statistics maintained by Snowflake 4. Use commands to read metadata or alter context (DESCRIBE, SHOW, USE) 5. Identify elements required for business goals using BI reports or SQL analysis - Enrich data by identifying and accessing relevant data from the Snowflake Marketplace 1. Create tables and views 2. Use Secure Data Sharing (Marketplace, Internal Marketplace, Private Listings, Listings) 3. Find external data sets that correlate with available data - Prepare data and load into Snowflake 1. Load data from external/internal stages into a table 2. Load files using Snowsight - Use a collection system to retrieve data 1. Synthetic Data Generation 2. Retrieve data from semi-structured sources (Parquet, Avro, ORC, JSON, XML) 3. Retrieve data from unstructured sources 4. Retrieve data from structured sources (CSV) - Implement data processing solutions 1. Cleanse, conform, and enrich data 2. Use logging and monitoring solutions (auditing, data lineage) 3. Automate and implement data pipelines (scheduling) 4. Respond to processing failures
Data Transformation and Data Modeling	22%-30%	- Design data models 1. Snowflake schema design 2. Data vault models 3. Star schema design - Transform data using SQL 1. QUALIFY clauses 2. Window functions 3. Common Table Expressions (CTEs) 4. PIVOT/UNPIVOT operations
Data Analysis	30%-32%	- Perform advanced analytics using SQL 1. Time-series analysis 2. Aggregate functions 3. Snowflake-specific analytical features
Data Presentation and Data Visualization	28%-29%	- Create dashboards 1. Present analytical results 2. Snowsight dashboards - Integrate with BI tools 1. Power BI integration 2. Tableau integration 3. Other partner visualization tools

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SnowPro Advanced: Data Analyst Certification Exam exam pdf guide & DAA-C01 prep sure exam

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q26-Q31):

NEW QUESTION # 26
A table named 'website_visits' tracks user activity with columns 'user_id', 'visit_timestamp' , and 'page_view'. You need to determine the time difference (in minutes) between each user's consecutive page views. Assuming 'visit_timestamp' is of data type TIMESTAMP NTZ, which Snowflake SQL query will accurately calculate this?

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

Answer: E

Explanation:
Option B is correct. start_time, end_timey calculates the difference in minutes between two timestamps. 1, visit_timestamp) OVER (PARTITION BY user_id ORDER BY visit_timestampy retrieves the previous timestamp for each user. The default value is crucial to handle the first visit for each user. Option A is incorrect because subtracting timestamps directly results in a value in days, not minutes. Option C is incorrect because 'TIMEDIFF does not exist in Snowflake. Option D uses 'TIMESTAMPDIFF but it does not accept the time unit as the first parameter. Option E uses AVG aggregation which is not the purpose of the window functions in this scenario.

NEW QUESTION # 27
A financial institution needs to build a dashboard to monitor fraudulent transactions. They have transaction data (amount, timestamp, location, merchant category), customer data (age, income, credit score), and fraud flags. They want to identify patterns and correlations that indicate potentially fraudulent activity. Which of the following visualizations and Snowflake features, when used in combination, would be MOST effective for this purpose? (Select TWO)

A. Develop an interactive dashboard using Power BI connected to Snowflake, incorporating a geographical map showing transaction locations, a time series chart of transaction volume, and a scatter plot of transaction amount vs. customer credit score. Use anomaly detection algorithms in Snowflake (via Snowpark or stored procedures using Python) to highlight unusual transactions. Implement User defined table Function in Power BI to generate more data.
B. Utilize a heatmap visualization in a Tableau dashboard to show the correlation between merchant category, transaction amount, and fraud flag. Implement interactive filters for customer age, income, and location. Implement Snowflake Row Access Policy to restrict data to specific users.
C. Create a static report showing the total number of fraudulent transactions per month, using Snowflake's aggregate functions.
D. Export the transaction data to a data lake and use Spark to perform fraud detection.
E. Use Snowflake's secure data sharing to share the raw transaction data with a third-party analytics vendor.

Answer: A,B

Explanation:
Options B and D are the most effective. Option B utilizes an interactive dashboard with Power BI, incorporating geographical, temporal, and scatter plot visualizations to identify patterns. Anomaly detection algorithms in Snowflake further enhance the detection capabilities. Option D uses a heatmap to show correlations between key variables and implements interactive filters for detailed exploration. Implementing Row Access Policy would control data access, adding a security layer on top of sensitive financial data. Option A is not related to visualization or identifying correlations. Option C provides only a high-level summary. Option E involves exporting data outside Snowflake, which is less efficient and potentially less secure than using Snowflake's built-in capabilities.

NEW QUESTION # 28
You are examining sales data for an e-commerce company in Snowflake. You notice a recurring dip in sales every Wednesday. The 'SALES' table contains columns: 'order_id', 'order date' , 'product_id', 'quantity', 'price'. Which of the following SQL queries and analytical techniques would be the MOST effective for diagnosing the reasons behind this weekly sales dip, assuming no external promotional activity always happens on Wednesdays?

A. Check if there are any scheduled maintenance windows or system outages occurring on Wednesdays that might be affecting order processing. Use 'INFORMATION SCHEMA.LOAD HISTORY' to look for any loads failing on wednesdays
B. Analyze sales patterns by 'product_id' and day of the week to see if specific product categories are driving the overall dip on Wednesdays. Use 'PIVOT' operator for cross-tabulation.
C. Run a query to calculate the average daily sales for each day of the week and visualize the results to confirm the dip on Wednesdays. Use "DAYOFWEEK' function for this.
D. Review recent code deployments related to the e-commerce platform and identify if any changes were implemented on Tuesdays or Wednesdays that could be impacting sales.
E. Investigate shipping times by day of the week to determine if longer shipping times on orders placed earlier in the week are affecting Wednesday sales. Join ' SALES table with 'SHIPPING' table and calculate difference between 'order_date' and 'ship_date' .

Answer: B,C,D,E

Explanation:
Options A, B, C, and E are the most relevant. Option A helps confirm the dip. Option B identifies product-specific patterns. Option C investigates a potential shipping-related cause. Option E explores potential software changes, while Option D focuses on a possible but less relevant cause of system maintenance.

NEW QUESTION # 29
You are tasked with cleaning a 'COMMENTS table that contains user-generated comments in a column (VARCHAR). The comments often contain HTML tags, excessive whitespace, and potentially malicious scripts. Your goal is to remove all HTML tags, trim leading and trailing whitespace, and escape any remaining HTML entities to prevent script injection vulnerabilities. Which combination of Snowflake scalar functions provides the most robust and secure way to achieve this data cleaning?

A. SELECT TRIM(REGEXP >', FROM COMMENTS;
B. SELECT >', FROM COMMENTS WHERE
C. SELECT >', FROM COMMENTS;
D. SELECT TRIM(HTML ENTITY DECODE(REGEXP >', FROM COMMENTS;
E. SELECT >', comment_text) FROM COMMENTS;

Answer: C

Explanation:
Option B is the most robust and secure method. Here's why: 'REGEXP REPLACE(comment_text, Y', "Y: This removes HTML tags. This attempts to parse the remaining text as XML. If there are still any unescaped or malformed HTML entities, this step will help to isolate them and get rid of the tags. If the text cannot be parsed as XML, PARSE_XML returns NULL. '$').$: This extracts the text content of the XML. Crucially, 'XMLGET' inherently performs HTML entity decoding, effectively escaping potentially dangerous characters (e.g., becomes This prevents script injection. This removes leading and trailing whitespace. Option A only removes the HTML tags and trims the text, but doesn't handle HTML entity encoding, and thus it is vulnerable to script injection. Option C is not correct as HTML ENTITY DECODE' is not an existing function in Snowflake. Option D is not correct as the text needs to be cleaned irrespective of whether it contains XML or not. Option E - if parsing the XML returns null then original value gets returned , which we don't want , we would need to make the value NULL.

NEW QUESTION # 30
How do stored procedures contribute to data analysis efficiency in SQL?

A. Stored procedures can't be used with UDFs.
B. Stored procedures enable custom and repetitive data operations, enhancing efficiency.
C. They only facilitate basic data summarization.
D. They restrict data accessibility for security purposes.

Answer: B

Explanation:
Stored procedures enhance efficiency by allowing custom and repetitive data operations.

NEW QUESTION # 31
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