Test DAA-C01 Valid | DAA-C01 Test Lab Questions

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Snowflake DAA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q14-Q19):

NEW QUESTION # 14
You are analyzing website traffic data in Snowflake to identify potential bot activity. You have a table 'WEB EVENTS' with columns 'event_timestamp' (TIMESTAMP NTZ), 'user_id' (VARCHAR), and 'ip_address' (VARCHAR). Which combination of SQL techniques and Snowflake features would be MOST effective in detecting and flagging suspicious bot-like behavior, considering high query performance and scalability?

A. Use a UDF (User-Defined Function) written in Python to perform complex behavioral analysis on user event sequences, checking for patterns like rapid page transitions or form submissions within unrealistic timeframes. Apply this UDF to the 'WEB EVENTS' table.
B. Create a scheduled task that periodically runs a query to analyze the ratio of human-generated events to server-generated events. If the ratio drops below a certain threshold, flag the time period as suspicious.
C. Calculate event frequency per user and IP address using window functions (e.g., 'COUNT() OVER (PARTITION BY user_id, ip_address ORDER BY Then, identify users/lPs with abnormally high event rates within short time intervals using appropriate threshold criteria.
D. Join the table with a publicly available list of known bot IP addresses. Flag any events originating from those IP addresses as potential bot activity. Supplement this with simple frequency counts of events per user.
E. Implement a stored procedure that iterates through each unique IP address in the table, calculating the average time between events for each 12 Flag IP addresses where the average time between events is significantly below a pre-defined threshold.

Answer: C,D

Explanation:
Options B and C offer a good balance of effectiveness and efficiency. Option B uses window functions, a powerful feature within Snowflake for analyzing data within a context (user and IP address). Option C uses a pre-defined list of bots and it is not resource intensive. Option A, while potentially accurate, can be computationally expensive due to the use of a UDF and might affect the overall cluster performance. Option D is better suited to detect DDoS attacks. Option E is inefficient as it iterates through the resultset

NEW QUESTION # 15
You have a Snowflake table containing order data'. You need to calculate the shipping cost for each order based on the order amount and the destination country. You decide to use a Java UDF for this calculation, as the logic is complex and involves external APIs (simulated here). The UDF should take the order amount (FLOAT) and destination country (VARCHAR) as input and return the calculated shipping cost (FLOAT). The Java code requires external JAR files to be imported. Which of the following options correctly defines and calls the Java UDF in Snowflake, assuming the necessary JAR file has been uploaded to a stage named 'my_stage'?

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

Answer: B

Explanation:
Option E is the most correct because the function definition does not require the definition of the class 'com.example.ShippingCalculator' within the function body. Since the jar file is defined within the imports section, snowflake does not need the explicit definition. Option A, C, and D requires the function and class definition which is already defined in the jar, and defining it again will lead to conflicts. Option B doesn't correctly define the class. All the rest of the options either try to define the Java code inline (which is incorrect when using IMPORTS) or have syntax errors in the UDF definition.

NEW QUESTION # 16
You are working with a dataset containing customer orders and their associated products. The data is stored in two tables: 'ORDERS' (order_id, customer_id, order_date) and 'ORDER ITEMS' (order_id, product_id, quantity, price). The Bl team needs to analyze the top- selling products and the average order value per customer. However, due to data quality issues, some 'order _ id' values in the 'ORDER ITEMS table do not exist in the 'ORDERS' table, leading to data inconsistencies. What is the most robust and efficient way to handle this data inconsistency issue while ensuring accurate reporting?

A. Create a flattened view that combines 'ORDERS' and 'ORDER_ITEMS' using a FULL OUTER JOIN. Handle missing values in the reporting layer.
B. Use a UNION ALL statement to combine the two tables, creating a single table with all order and item information. Handle inconsistencies during data extraction and loading (ETL) processes.
C. Use an INNER JOIN between 'ORDERS' and 'ORDER ITEMS'. Before joining, use a subquery or CTE to filter 'ORDER ITEMS' to include only 'order_id' values that exist in 'ORDERS'. After joining apply a filter for valid prices(Price > 0). This ensures correct analysis and avoids invalid orders. Select all the rows with valid prices (Price > 0).
D. Use a RIGHT JOIN from 'ORDERS' to 'ORDER ITEMS' and filter out rows where order details are missing.
E. Use a LEFT JOIN from 'ORDERS' to 'ORDER ITEMS to include all orders, even if they don't have corresponding items. Filter out rows with NULL 'product_id' in the final result.

Answer: C

Explanation:
Option B provides the most robust and efficient solution. By using an INNER JOIN after filtering the 'ORDER_ITEMS' table to include only valid 'order_id' values (those present in the 'ORDERS' table), you ensure that the analysis is based on consistent and reliable data. The subquery/CTE effectively cleans the data before joining, preventing inaccurate calculations. Filtering for valid prices ensures data quality. Option A would include orders without item details, which is not desired for this analysis. Options C and D might result in inaccurate results due to the incomplete relationship between orders and products. Option E is not suitable since it mixes the distinct column sets, resulting in inaccurate analyses. Always ensure referential integrity where business logic dictates.

NEW QUESTION # 17
You are analyzing the query execution plan of a complex data transformation pipeline in Snowflake. The plan shows a 'Remote Join' operation with high execution time. The two tables involved, 'CUSTOMER and 'ORDERS' , reside in different Snowflake accounts, and the join is performed on the 'CUSTOMER ID' column. Which of the following actions would MOST effectively optimize this query and reduce the 'Remote Join' execution time?

A. Ensure both 'CUSTOMER and 'ORDERS tables have the same clustering key, prioritizing 'CUSTOMER IDS.
B. Replicate the smaller table (either 'CUSTOMER or 'ORDERS, based on size) to the same Snowflake account as the larger table to eliminate the remote join.
C. Increase the warehouse size of the account containing the 'ORDERS' table to improve its processing speed.
D. Create a materialized view in the ORDERS account that pre-aggregates the data needed for the join to reduce the data size sent over the network for remote join.
E. Implement data filtering on the 'CUSTOMER table before the 'Remote Join' to reduce the amount of data transferred across accounts. Using temporary table can be used for this task.

Answer: B,E

Explanation:
Options B and C are the most effective. B eliminates the need for a remote join altogether, and C reduces the amount of data transferred during the remote join. Clustering keys (A) don't directly affect remote joins in the same way they affect local joins. Increasing warehouse size (D) can improve performance but doesn't address the fundamental issue of the remote join data transfer. Option E can help if the aggregated data fulfills the query's requirement and reduces significant data transfer, so it might be partially correct, but replicating data or filtering before joining is optimal in most cases.

NEW QUESTION # 18
You are working with a Snowflake table 'raw_data' containing a column of type TEXT that stores log messages in various formats, including JSON and CSV. You need to extract specific data points from these log entries, treating JSON entries differently from CSV entries. Specifically: For JSON log entries (identified by starting with '{l), extract the value of the 'user_id' key. For CSV log entries (identified by starting with a number), extract the second field (assuming comma-separated values). Which of the following queries is the most efficient and correct way to achieve this data extraction?

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

Answer: B

Explanation:
Option B provides the most efficient and correct solution. 1, 1) = '{' ' efficiently checks if the log entry starts with '{' without needing the full power of STARTS_WITH or PARSE_JSON. For JSON entries, it correctly uses to extract the 'user_id' directly from the VARIANT representation. For CSV entries, it uses ',', 2)' which is specifically designed to extract a single part from a delimited string, and is more efficient than using SPLIT. Option A will not treat the JSON objects in the 'log_entry' column as valid variants and needs to be parsed using PARSE_JSON explicitly to get correct answer. Option C uses GET _ PATH which cannot be used without parsing the JSON first. Option D will error out because you cannot use JSON dot notation on a TEXT column directly. Option E uses SPLIT TO TABLE which is more costly than SPLIT PART.

NEW QUESTION # 19
......

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