PREMIUM SNOWFLAKE DAA-C01 FILES - DAA-C01 STUDY GUIDE

Premium Snowflake DAA-C01 Files - DAA-C01 Study Guide

Premium Snowflake DAA-C01 Files - DAA-C01 Study Guide

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q146-Q151):

NEW QUESTION # 146
A telecommunications company wants to segment its customers based on their usage patterns for targeted marketing campaigns. You have access to a table named 'CUSTOMER USAGE with the following columns: 'CUSTOMER ONT), 'DATA USAGE GB' (FLOAT), 'VOICE CALL MINUTES (INT), and (INT). Which of the following Snowflake features or techniques would be MOST appropriate for performing customer segmentation and determining distinct customer clusters?

  • A. Using Snowflake's built-in 'QUALIFY' clause combined with window functions to rank customers based on individual usage metrics and categorize them based on rank percentiles.
  • B. Utilizing Snowflake's external functions to call a machine learning model hosted on a platform like AWS SageMaker or Azure Machine Learning to perform the clustering and return the segment assignments.
  • C. Creating a series of complex SQL queries with multiple 'CASE statements to manually define customer segments based on predefined thresholds for data usage, voice calls, and SMS count.
  • D. Implementing a K-Means clustering algorithm using Snowflake's Python User-Defined Functions (UDFs) and storing the cluster assignments in a new column within the 'CUSTOMER USAGE table.
  • E. Using the 'APPROX COUNT DISTINCT function to estimate the number of distinct usage patterns without performing actual clustering.

Answer: B,D

Explanation:
Options B and E are the most appropriate. Option B leverages Snowflake's UDF capabilities for in-database processing, allowing for potentially complex custom clustering algorithms. Option E allows integration with external machine learning platforms to take advantage of pre- built, optimized machine learning models. Option A is not appropriate because it just provides a count of distinct patterns, not the clustering itself. Option C is not scalable or maintainable for complex segmentation. Option D provides ranking, but not clustering or segmentation in the sense intended by the question.


NEW QUESTION # 147
A data engineer is tasked with auditing changes made to the 'EMPLOYEES' table over the past week. The data retention period for the Snowflake account is set to 7 days. They need to identify all the rows that were updated or deleted during this period. Which of the following approaches, utilizing Snowflake's Time Travel capabilities, will efficiently provide the required audit information, assuming no custom metadata tracking was implemented?

  • A. Query the 'EMPLOYEES' table using the 'AT(OFFSET => -604800)' clause and compare the results with the current state of the table to identify changes.
  • B. Use the 'VALIDATE' table functionality combined with Time Travel to identify modified rows within the last week.
  • C. Create a clone of the 'EMPLOYEES' table from a week ago using Time Travel, and then perform a full outer join between the clone and the current table, identifying differences based on NULL values in either table.
  • D. Enable Snowflake's Change Data Capture (CDC) functionality. Use the stream object to obtain all updates to 'EMPLOYEES' table, then use Time Travel for the stream data to access the required data.
  • E. Query the 'QUERY_HISTORY view in the ACCOUNT _ USAGE schema to identify all UPDATE and DELETE statements executed against the 'EMPLOYEES' table in the past week, then use Time Travel with specific query IDs to retrieve the affected rows.

Answer: E

Explanation:
Option C provides the most efficient and comprehensive approach. By querying the 'QUERY HISTORY view, the engineer can identify the specific queries that modified the 'EMPLOYEES table. Then, using Time Travel with the query IDs, they can retrieve the state of the table before those queries were executed, allowing for a precise comparison and identification of the affected rows. Option A is too broad; it provides a snapshot of the table a week ago but doesn't pinpoint specific changes. Option B doesn't allow data extraction of the changes, so incorrect. Option D is computationally expensive and may not be accurate for large tables. Option E is not feasible because the table was already updated, and CDC needs to be setup before the event. Setting up a stream on a historical data point is not possible.


NEW QUESTION # 148
You are tasked with loading a large CSV file containing website traffic data into Snowflake. The CSV file has the following characteristics: Header row is present. Fields are enclosed in double quotes. The delimiter is a pipe (l) character. One column, 'timestamp' , is stored as milliseconds since the epoch and needs to be converted to a Snowflake TIMESTAMP NTZ. Which of the following COPY INTO statement options would correctly load the data, handle the delimiter and quotes, and convert the 'timestamp' column?

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

Answer: C

Explanation:
Option E is correct because it correctly handles the milliseconds since epoch conversion. It casts the 'timestamp' column to a to ensure accurate division and then divides by 1000 to convert milliseconds to seconds before applying TO_TIMESTAMP_NTZ. Option A is incorrect because timestamp could be a String. Option B is incorrect as date format is not relevant here and it doesn't divide by 1000. Option C is incorrect because BIGINT might not be sufficient for large timestamps. Option D is incorrect because = 'EPOCH MILLIS" is used in file format options, not transform column.


NEW QUESTION # 149
You have a table named 'event_data' that tracks user activities. The table contains 'event_id' (INT), 'user _ id' (INT), (TIMESTAMP NTZ), 'event_type' (VARCHAR), and 'event_details' (VARIANT). The table is partitioned by Performance on queries filtering by both 'event_type' and a specific date range on is slow You suspect inefficient partition pruning and JSON parsing as potential bottlenecks. Which combination of actions will most effectively address these performance issues?

  • A. Create a view that extracts specific fields from the 'event_details' column into separate columns and add a secondary index on 'event_type' .
  • B. Add a masking policy on the 'event_details' column and recluster the table by 'user_id'.
  • C. Create a temporary table containing the results and then performing a Merge operation.
  • D. Change the partition key to 'event_type' and create a table function to query sevent_detailss.
  • E. Create a materialized view partitioned by and clustered by 'event_type' , pre-extracting relevant fields from 'event_detailS into separate columns.

Answer: E

Explanation:
Option B provides the most effective solution. Creating a materialized view addresses both problems: Partitioning by ensures efficient partition pruning when querying by date ranges. Clustering by 'event_type' improves performance when filtering on this column. Pre-extracting fields from sevent_detailS into separate columns avoids expensive JSON parsing at query time. Option A, adding index will not perform better than partition pruning. Option C changing partition key will require full reload of data and clustering table is expensive. Option D, masking policy will secure sensitive data but won't resolve performance issues. Option E, creating a temporary table and performing a Merge operation will increase cost and time.


NEW QUESTION # 150
A data analyst needs to process a large JSON payload stored in a VARIANT column named 'payload' in a table called 'raw events' The payload contains an array of user sessions, each with potentially different attributes. Each session object in the array has a 'sessionld' , 'userld' , and an array of 'eventS. The events array contains objects with 'eventType' and 'timestamp'. The analyst wants to use a table function to flatten this nested structure into a relational format for easier analysis. Which approach is most efficient and correct for extracting and transforming this data?

  • A. Create a recursive UDF (User-Defined Function) in Python to traverse the nested JSON and return a structured result, then call this UDF in a SELECT statement.
  • B. Utilize a Snowpark DataFrame transformation with multiple 'explode' operations and schema inference to flatten the nested structure and load data into a new table.
  • C. Load the JSON data into a temporary table, then write a series of complex SQL queries with JOINs and UNNEST operations to flatten the data.
  • D. Employ a combination of LATERAL FLATTEN and Snowpark DataFrames, using LATERAL FLATTEN to partially flatten the JSON and then Snowpark to handle the remaining complex transformations and data type handling.
  • E. Use LATERAL FLATTEN with multiple levels of nesting, specifying 'path' for each level and directly selecting the desired attributes.

Answer: E

Explanation:
Option A is the most efficient and Snowflake-native approach. LATERAL FLATTEN is optimized for handling nested data structures within Snowflake. While other options might work, they introduce overhead (UDF execution), are less efficient (temporary tables and complex SQL), or rely on external frameworks (Snowpark), making them less suitable for this scenario. Specifying the path ensures specific fields are targeted, avoiding unnecessary processing of irrelevant data. LATERAL flatten allows you to join the output of a table function with each row of the input table. This is essential to maintain the context (e.g., userId) from the outer table.


NEW QUESTION # 151
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