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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q27-Q32):
NEW QUESTION # 27
You are planning to monetize a dataset on the Snowflake Marketplace. You want to provide potential customers with sample data to evaluate before they purchase a full subscription. Which of the following strategies are valid and recommended for offering a free sample of your data within the Snowflake Marketplace? (Select all that apply)
Answer: B,C
Explanation:
Option A is valid: Creating a separate share with a subset of the data is a common and secure way to offer a free sample. Option C is valid: sharing a view filtered by a sampling algorithm offers a representative sample without requiring manual data management. Option B is less ideal because managing trial expiration and access controls requires custom coding and increases complexity. Offering consumers a database link (Option D) is a valid concept but less secure than using Snowflake's native sharing mechanisms for trial access. Option E is less integrated with the Marketplace experience; the consumer must leave Snowflake to access the sample data, and there is no tracking of who is accessing the sample.
NEW QUESTION # 28
You are working with a directory table named associated with an external stage containing a large number of small JSON files. You need to process only the files containing specific sensor readings based on a substring match within their filenames (e.g., files containing 'temperature' in the filename). You also want to load these files into a Snowflake table 'sensor_readings. Consider performance and cost-effectiveness. Which of the following approaches is the MOST efficient and cost-effective to achieve this? Choose TWO options.
Answer: B,C
Explanation:
Options B and C are the most efficient and cost-effective. Option B (Create a view and use COPY INTO with FILES): Creating a view that filters the directory table allows you to isolate the relevant filenames. Then, using 'COPY INTO' with the 'FILES' parameter pointing to this filtered view directly instructs Snowflake to load only the specified files, minimizing unnecessary data processing. This is efficient as it leverages Snowflake's built-in capabilities. Option C (COPY INTO with the PATTERN parameter): The 'PATTERN' parameter within the 'COPY INTO' command allows you to specify a regular expression. By incorporating the substring match into this regular expression against the metadata$filename" , you can directly filter which files are loaded during the 'COPY INTO operation. This avoids loading irrelevant data and is generally more performant than iterating through files using a UDF. Other options are less efficient or less cost-effective: Option A (Python UDF): Using a Python UDF for this task is generally less efficient. Snowflake is designed to handle this processing natively, and using UDF can lead to performance overhead due to data serialization and deserialization between Snowflake and the UDF environment. Option D (Load all and filter later): Loading all files into a staging table and then filtering is wasteful. It increases data processing time and costs since you're loading unnecessary data. It's always better to filter data closer to the source if possible. Option E (Masking Policy): Masking policies are for security, not data transformation. They are applied at the query level to prevent users from seeing data, but do not help in efficiently processing only specific files.
NEW QUESTION # 29
You have a Snowflake table called 'RAW ORDERS that contains semi-structured JSON data in a column named 'ORDER DETAILS. You need to extract specific fields from the JSON data, perform some data type conversions, and then load the transformed data into a relational table named 'CLEAN ORDERS'. Your requirements are as follows: 1. Extract the (STRING) from the JSON and store it as 'ORDER ID (NUMBER). 2. Extract the (STRING) from the JSON and store it as 'CUSTOMER ID (NUMBER). 3. Extract the 'order_date' (STRING) from the JSON and store it as 'ORDER DATE' (DATE). 4. Extract (STRING) from the JSON and store it as 'TOTAL AMOUNT' (FLOAT). Which of the following Snowpark Python code snippets correctly transforms the data and loads it into the 'CLEAN ORDERS table using a combination of Snowpark DataFrame operations and SQL? Assume that session 'sp' is already initialized.
Answer: E
Explanation:
Option A correctly uses Snowpark to extract and transform the JSON data. It utilizes the correct syntax for extracting values from the column using the colon notation It then uses 'to_date', and 'to_float' to perform the necessary data type conversions. Finally, it saves the transformed DataFrame to a new table called 'CLEAN ORDERS' using overwrite mode. Option B uses incorrect syntax with 'col('ORDER_DETAlLS:order_id')'. Option C uses the 'get()' function, which is more appropriate when the JSON structure is unknown, and using colon notation is simpler and faster when the JSON structure is known. Option D attempts to use , which returns a JSON object that can be accessed with square brackets. The correct Snowflake function to access JSON properties in this manner without parsing, is through the colon notation. Option E uses which is a Snowflake SQL function, not a native Snowpark function, and requires JSON path, making it less efficient than direct extraction using Although E will work, A is more optimized way to write code.
NEW QUESTION # 30
You are troubleshooting a slowly performing query in Snowflake that aggregates data from a large ORDERS table (10 billion rows) partitioned by ORDER DATE. The query execution plan shows significant 'Remote Spill to Disk'. Which of the following actions would be MOST effective in reducing the spill and improving query performance? Assume all statistics are up-to-date and the data is properly clustered by ORDER_DATE.
Answer: B
Explanation:
Remote Spill to Disk indicates that Snowflake is running out of memory to process the query. Increasing the virtual warehouse size (A) can help, but focusing on data pruning (E) is often more effective. By filtering on ORDER_DATE, you drastically reduce the amount of data that needs to be processed, minimizing memory usage and the need to spill to disk. Options B, C, and D are less likely to directly address the spill issue. doesn't exist. Window functions don't inherently resolve spills. Reducing selected columns can help slightly but is usually less impactful than data pruning.
NEW QUESTION # 31
You have a Snowflake table, 'CUSTOMER ORDERS', with columns like 'CUSTOMER ID', 'ORDER DATE', 'ORDER AMOUNT', and 'REGION'. A Bl dashboard relies on a query that aggregates data from this table, but the query performance is consistently slow. The query frequently filters by 'ORDER DATE and groups by 'REGION'. Based on the following 'EXPLAIN' output, which combination of techniques should be considered to improve the performance the most?
Answer: A,D
Explanation:
The ' EXPLAIN' output shows that the 'TableScan' operation has a high cardinality (100000000 rows), indicating that Snowflake is scanning a large portion of the table. The 'Filter' operation also has a high cardinality (1000000 rows). Creating a materialized view (A) that pre- aggregates the data by 'ORDER_DATE and 'REGION' can significantly improve performance by reducing the amount of data that needs to be scanned and aggregated at query time. Clustering the table on and 'REGION' (C) can also improve performance by organizing the data in a way that allows Snowflake to prune more data during the table scan. Indexes are not supported, increasing warehouse size provides more compute resources but is less effective than optimizing the data organization or query strategy. Redesigning dashboard can help with presentation layer but not at Database layer.
NEW QUESTION # 32
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