FREE PDF QUIZ 2025 SNOWFLAKE DEA-C02 PERFECT LATEST TEST EXPERIENCE

Free PDF Quiz 2025 Snowflake DEA-C02 Perfect Latest Test Experience

Free PDF Quiz 2025 Snowflake DEA-C02 Perfect Latest Test Experience

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q187-Q192):

NEW QUESTION # 187
You are tasked with building a data pipeline to process image metadata stored in JSON format from a series of URLs. The JSON structure contains fields such as 'image_url', 'resolution', 'camera_model', and 'location' (latitude and longitude). Your goal is to create a Snowflake table that stores this metadata along with a thumbnail of each image. Given the constraints that you want to avoid downloading and storing the images directly in Snowflake, and that Snowflake's native functions for image processing are limited, which of the following approaches would be most efficient and scalable?

  • A. Create a Python-based external function that fetches the JSON metadata and image from their respective URLs. The external function uses libraries like PIL (Pillow) to generate a thumbnail of the image and returns the metadata along with the thumbnail's Base64 encoded string within a JSON object.
  • B. Create a Snowflake view that selects from a table containing the metadata URLs, using 'SYSTEM$URL GET to fetch the metadata. For each image URL found in the metadata, use a JavaScript UDF to generate a thumbnail. Embed the thumbnail into a VARCHAR column as a Base64 encoded string.
  • C. Store just the 'image_url' in snowflake. Develop a separate application using any programming language to pre generate the thumbnails and host those at publicly accessible URLs. Within Snowflake, create a view to generate the links for image and thumbnail using 'CONCAT.
  • D. Create a Snowflake external table that points to an external stage which holds the JSON metadata files. Develop a spark process to fetch image URL, create thumbnails and store as base64 encoded strings in an external stage, create a view using the external table and generated thumbnails data
  • E. Create a Snowflake stored procedure that iterates through each URL, downloads the JSON metadata using 'SYSTEM$URL_GET, extracts the image URL from the metadata, downloads the image using 'SYSTEM$URL_GET , generates a thumbnail using SQL scalar functions, and stores the metadata and thumbnail in a Snowflake table.

Answer: A,C

Explanation:
Option C is the most appropriate solution. By using an external function with Python and libraries like PIL, you can efficiently handle image processing tasks that are difficult or impossible to perform natively within Snowflake. The external function encapsulates the image processing logic, keeping the Snowflake SQL code cleaner. Option E is also a valid solution as it leverages external processing. Option A is not performant as it tries to download image in snowflake which is not the best way to process image. Option B is not recommended because using JavaScript UDFs for binary data (images) can be inefficient. External Tables as described in Option D require pre-processing of data and storage to an external stage. Option D doesn't use the 'SYSTEM$URL GET' function that this question is trying to assess.


NEW QUESTION # 188
You are developing a Snowpark Python application that processes data from a large table. You want to optimize the performance by leveraging Snowpark's data skipping capabilities. The table 'CUSTOMER ORDERS is partitioned by 'ORDER DATE. Which of the following Snowpark operations will MOST effectively utilize data skipping during data transformation?

  • A. Applying a filter >= '2023-01-01') & (col('ORDER_DATE') <= '2023-03-31'))' after performing a complex join operation.
  • B. Using the 'cache()' method on the DataFrame before filtering by 'ORDER DATE
  • C. Creating a new DataFrame with only the columns needed using 'ORDER_DATE', 'ORDER_AMOUNT')' before any filtering operations.
  • D. Executing 'df.collect()' to load the entire table into the client's memory before filtering.
  • E. Applying a filter '2023-01-01') & '2023-03-31'))' before performing any join or aggregation operations.

Answer: E

Explanation:
Option C is the most effective. Data skipping works best when filters are applied early in the query execution plan. By filtering on the partition column CORDER DATE) before any joins or aggregations, Snowflake can effectively skip irrelevant partitions, significantly reducing the amount of data scanned. Applying the filter after joins (Option A) defeats the purpose of data skipping. Selecting columns (Option B) doesn't directly utilize data skipping. Caching (Option D) might help with subsequent operations but doesn't leverage data skipping itself. Collecting data (Option E) is highly inefficient for large tables and bypasses any server-side optimizations.


NEW QUESTION # 189
A data provider wants to share a large dataset (several TB) with multiple consumers. The dataset is updated daily. The provider wants to minimize the cost associated with data sharing and ensure that consumers receive consistent data'. Which of the following strategies would be the MOST cost-effective and maintainable?

  • A. Create a data share with views that point to the base tables. Use time travel to allow consumers to query data from a specific point in time before the daily update.
  • B. Create a data share and create a separate database for each consumer, cloning the data into each consumer's database daily.
  • C. Create a data share and grant access to all consumers directly on the base tables.
  • D. Create a data share containing external tables pointing to data stored in cloud storage (e.g., AWS S3), updated daily using a pipeline.
  • E. Create a data share with views that point to the base tables, and clone the base tables daily into a separate 'staging' database before sharing.

Answer: A

Explanation:
Using time travel allows consumers to query a consistent snapshot of the data before the daily updates without requiring expensive data cloning or maintaining separate databases for each consumer. Cloning data daily (Options B and D) is extremely costly. Sharing the base tables directly (Option A) may not be desirable due to security or management considerations. Using external tables (option E) could introduce latency or require the consumer to manage their own compute resources.


NEW QUESTION # 190
You have a data pipeline that aggregates web server logs hourly. The pipeline loads data into a Snowflake table 'WEB LOGS' which is partitioned by 'event_time'. You notice that queries against this table are slow, especially those that filter on specific time ranges. Analyze the following Snowflake table definition and query pattern and select the options to diagnose and fix the performance issue: Table Definition:

  • A. The table is already partitioned by 'event_time' , so there is no need for further optimization.
  • B. Change the table to use clustering on 'event_time' instead of partitioning to improve query performance for range filters.
  • C. Create a materialized view that pre-aggregates the 'status_code' by hour to speed up the aggregation query.
  • D. Increase the warehouse size to improve query performance.
  • E. Add a search optimization strategy to the table on the 'event_time' column.

Answer: B,C,E

Explanation:
Partitioning in Snowflake is primarily for data management and micro-partition elimination on exact matches, not range queries. Clustering (B) reorders the data for better performance with range-based queries. A materialized view (C) pre-computes the aggregation, significantly speeding up the specific query. A search optimization strategy (E) can improve performance without requiring a full table scan. Increasing warehouse size (D) may help to some extent but is not the most targeted optimization. Option A is incorrect because partitioning alone doesn't solve the range query performance issue.


NEW QUESTION # 191
You have a Snowflake stage pointing to an external cloud storage location containing numerous Parquet files. A directory table is created on top of it. Over time, some files are deleted or moved from the external location. You notice discrepancies between the directory table's metadata and the actual files present in the storage location. Choose the option that best describes how Snowflake handles these discrepancies and the actions you should take.

  • A. Snowflake automatically detects deleted files and marks them as 'invalid' in the directory table. Queries will automatically exclude these invalid files.
  • B. Snowflake requires you to drop and recreate the directory table periodically to synchronize the metadata with the external storage. Using 'ALTER DIRECTORY TABLE REFRESH' will not remove deleted files from the directory table's metadata. However, these invalid files wont be shown in select unless explicitly used.
  • C. Snowflake does not track file deletions. If a file is deleted from cloud storage after being added to a directory table, Snowflake continues to reference the deleted file, potentially causing errors during data loading. Run 'VALIDATE on the directory table.
  • D. Snowflake does not automatically detect these changes. You must manually refresh the directory table using 'ALTER DIRECTORY TABLE ... REFRESH' to synchronize the metadata. Snowflake does not provide an automated cleanup of metadata associated with removed files.
  • E. Snowflake automatically updates the directory table in real-time, reflecting the changes immediately. No action is needed.

Answer: C

Explanation:
Snowflake's directory tables do not automatically reflect changes in external storage due to file deletions. The 'ALTER DIRECTORY TABLE ... REFRESH' command updates the metadata, but it doesn't automatically remove entries for deleted files. Attempting to load data from a deleted file will likely result in errors. Running 'VALIDATE on the directory table will identify the files that no longer exist in the external stage. Options A, B, C, and D are incorrect because they misrepresent how Snowflake manages changes in the underlying storage.


NEW QUESTION # 192
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