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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q209-Q214):

NEW QUESTION # 209
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 >', FROM COMMENTS;
B. SELECT TRIM(HTML ENTITY DECODE(REGEXP >', FROM COMMENTS;
C. SELECT >', comment_text) FROM COMMENTS;
D. SELECT >', FROM COMMENTS WHERE
E. SELECT TRIM(REGEXP >', FROM COMMENTS;

Answer: A

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 # 210
Consider a 'customer_orders' table with 'customer_id' , 'order_date', and 'order_amount'. You need to identify customers who have placed orders consistently over the last 3 months, specifically, you need to find customers who have placed an order in each of the last 3 months (including the current month). Assume the current date is '2024-01-15'. Which of the following query snippets, when incorporated into a complete query, would be most efficient and accurate for identifying these customers?

Answer: A

Explanation:
Option E is the most precise and efficient. It explicitly checks if a customer has an order in each of the three specific months (November, December, January). It does this by truncating the 'order_date' to the beginning of the month using 'DATE TRUNC('MONTH', order_datey and then comparing against the truncated values for the last three months calculated using 'DATEADD. The 'SUM' will only be equal to 3 if the customer has at least one order in each of those months. Option A calculates the number of distinct months for each customer but doesn't guarantee they are the last 3 months. Option B checks if the customer has placed at least 3 orders in the last 3 months, but it might be that all 3 orders are in a single month. Option C doesn't count distinct months. Option D only returns 1 if the customer has placed an order in the last 3 months. It does not guarantee the customer placed an order in all the past 3 months.

NEW QUESTION # 211
You are a data analyst for an e-commerce company. You need to create a dashboard visualizing sales performance. The dashboard requires two key features: 1) users should be able to filter the data by product category dynamically using a dropdown list. 2) The dashboard should efficiently handle large datasets (millions of rows) without performance degradation. Which Snowflake features and techniques would you use to achieve these requirements most effectively?

A. Implement user-defined functions (IJDFs) in Python to perform custom filtering logic, and store the results in a temporary table for the dashboard to consume.
B. Extract all data into a pandas DataFrame and create a Dash application for the front end, as Snowflake struggles to handle real-time filtering effectively.
C. Use Snowflake's dynamic data masking to hide sensitive sales data based on user roles, and create a view with pre-aggregated data for each product category to improve dashboard performance.
D. Create a stream on the sales data and use a task to continuously update a summary table with aggregated data for each product category. Use the summary table as the data source for the dashboard.
E. Utilize Snowflake's caching mechanisms and create materialized views to pre-compute aggregated data for the dashboard, along with using dashboard-level filtering widgets connected to the product category column.

Answer: E

Explanation:
Using materialized views pre-computes aggregates and leverages Snowflake's caching. Dashboard-level filtering (option C) allows for dynamic filtering without querying the base data directly, providing the best performance and scalability. Options A and D are less ideal for real-time filtering; B is an anti-pattern in Snowflake, and E moves data out of Snowflake unnecessarily.

NEW QUESTION # 212
You are tasked with ingesting data from a variety of external sources (JSON, CSV, Parquet) into Snowflake to build a unified customer profile. The sources contain inconsistencies in data types, null handling, and naming conventions. Your goal is to create a robust ingestion pipeline that identifies data quality issues and prepares the data for analysis. Which combination of Snowflake features and approaches would be most effective for achieving this?

A. Employ a third-party ETL tool integrated with Snowflake, configure data cleansing and transformation rules within the ETL tool, and load the transformed data into Snowflake using its optimized connectors.
B. Leverage Snowflake's data governance features with data profiling, implement data quality rules in a data catalog, and use a combination of Snowpipe and COPY INTO with data transformations within the COPY INTO statement.
C. Utilize Dynamic Tables with appropriate transformation logic defined to handle inconsistencies and maintain data quality during ingestion. Leverage Snowflake's data governance features in conjunction with Dynamic Tables for comprehensive data management
D. Use Snowpipe with schema evolution enabled and rely solely on Snowflake's automatic data type conversion. Implement data quality checks in downstream views.
E. Build a custom data ingestion application using the Snowflake Connector for Python, implementing complex data transformations and quality checks within the application before loading the data into Snowflake. Store raw data as is for future use.

Answer: A,B,C,E

Explanation:
Options B, C, D, and E represent robust approaches. B combines data governance and built-in transformations. C allows for custom logic using the Python connector. D uses a specialized ETL tool. E leverages Dynamic Tables for automated transformations and data quality maintenance. A is insufficient as it relies too heavily on automatic conversion and delays quality checks. The best approach would involve data profiling to understand the data, implementing data quality rules at the point of ingestion or through dynamic tables, and leveraging a transformation approach suited to the complexity of the transformations.

NEW QUESTION # 213
You are tasked with identifying PII (Personally Identifiable Information) within several tables in your Snowflake data warehouse before granting access to a new analytics team. You have a database called 'CUSTOMER DATA with tables 'CUSTOMERS' , 'ADDRESSES' , and 'ORDERS'. Which of the following SQL queries, leveraging Snowflake's information schema, would be the most efficient and least intrusive method to discover potential PII columns, assuming you have a naming convention where PII columns often contain terms like 'EMAIL', 'PHONE', 'SSN', or 'NAME'?

Answer: A

Explanation:
The correct answer is E. It leverages the 'information_schema.columns' view, which is a standard and efficient way to retrieve metadata in Snowflake. using 'table_schema = 'CUSTOMER_DATA" and 'table_name IN ('CUSTOMERS', 'ADDRESSES', 'ORDERS')' filters the results to the specific database and tables of interest, making it more efficient than scanning all columns in the account or using table_catalog'. ensures case-insensitive matching. Options A and D uses either wrong schema or wrong like. Option B and C uses account_usage, that can introduce delay to data availability since that is for billing and monitoring and also does not filter by table names

NEW QUESTION # 214
......

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PASS GUARANTEED QUIZ DAA-C01 - SNOWPRO ADVANCED: DATA ANALYST CERTIFICATION EXAM PERFECT LATEST EXAM FORMAT

Pass Guaranteed Quiz DAA-C01 - SnowPro Advanced: Data Analyst Certification Exam Perfect Latest Exam Format