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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q50-Q55):

NEW QUESTION # 50
You have created a Snowflake Iceberg table that points to data in an AWS S3 bucket. After some initial data ingestion, you realize that the schema in the Iceberg table does not perfectly match the schema of the underlying Parquet files in S3. Specifically, one of the columns in the Iceberg table is defined as 'VARCHAR , while the corresponding column in the Parquet files is stored as 'INT. What will be the most likely behavior when you query this Iceberg table in Snowflake?

A. Snowflake will automatically cast the SINT' data in the Parquet files to 'VARCHAR during query execution, and the query will succeed without any errors or warnings.
B. Snowflake will attempt to cast the data, and if a cast fails (e.g., 'INT' value is too large to fit in 'VARCHAR), the query will return an error only for those specific rows. Other rows will be processed correctly.
C. The query will fail with an error indicating a data type mismatch between the Iceberg table schema and the underlying Parquet file schema.
D. The query will succeed, but the result will be unpredictable and may vary depending on the specific data values in the Parquet files.
E. The query will succeed, but the 'VARCHAR column will contain 'NULL' values for all rows where the underlying Parquet files contain 'INT' values.

Answer: C

Explanation:
Snowflake enforces schema validation for Iceberg tables. If the data types in the Iceberg table schema do not match the data types in the underlying Parquet files, the query will fail with an error. This is because Snowflake relies on the Iceberg metadata to understand the data types and structure of the data in the Parquet files. A mismatch indicates a problem with the Iceberg table definition or the underlying data and should be corrected to ensure data integrity. While Snowflake is often flexible with implicit casting, in the context of Iceberg tables and schema enforcement, a type mismatch will lead to a query failure.

NEW QUESTION # 51
You are building a data pipeline in Snowflake using Snowpark Python. As part of the pipeline, you need to create a dynamic SQL query to filter records from a table named 'PRODUCT REVIEWS based on a list of product categories. The list of categories is passed to a stored procedure as a string argument, where categories are comma separated. The filtered data needs to be further processed within the stored procedure. Which of the following approaches are MOST efficient and secure ways to construct and execute this dynamic SQL query using Snowpark?

A. Constructing the SQL query using 'session.sql()' and string concatenation, ensuring proper escaping of single quotes within the product categories string.
B. Using Python's string formatting along with the and 'session.sql()' functions to build and execute the SQL query securely, avoiding SQL injection vulnerabilities.
C. Using the Snowpark "functions.lit()' function to create literal values from the list of product categories and incorporating them into the SQL query, then use 'session.sql()' to run it.
D. Using Snowpark's on the list of product categories after converting them into a Snowflake array, and then using 'session.sql()' to execute the query.
E. Using Python's string formatting to build the SQL query directly, and then executing it using 'session.sql()'.

Answer: B,C

Explanation:
Options C and E are the most appropriate and secure. Option C leverages 'snowflake.snowpark.functions.lit()' to safely incorporate literal values into the SQL query. The 'lit()' function handles the proper escaping, mitigating potential SQL injection risks. The resulting query is then executed with 'session.sql()'. This solution is very useful when using 'SP' and 'UDTF. Option E uses to create a SQL expression object that safely constructs the 'WHERE clause using string formatting, reducing the risk of SQL injection. session.sql()' is then used to execute the constructed query. Options A and B are generally unsafe due to SQL injection vulnerabilities. Option D may not be efficient for larger category lists and is less readable.

NEW QUESTION # 52
You are designing a data pipeline to load JSON data from an AWS S3 bucket into a Snowflake table. The JSON files have varying schemas, and you want to use schema evolution to handle changes. You are using a named external stage with 'AUTO REFRESH = TRUE. You notice that some files are not being ingested, and the COPY HISTORY shows 'Invalid JSON' errors. Which of the following actions would BEST address this issue while minimizing manual intervention?

A. Modify the COPY INTO statement to include 'ON ERROR = SKIP FILE' to ignore files with invalid JSON and continue loading other files. This ensures the pipeline continues without interruption.
B. Implement a pre-processing step using a Snowpark Python UDF to cleanse the JSON files in the stage before the COPY INTO command is executed. This UDF should handle schema variations and correct any invalid JSON structures.
C. Create a separate landing stage for potentially invalid JSON files and use a task to validate the files before moving them to the main stage for ingestion into Snowflake.
D. Re-create the stage with the 'AUTO REFRESH = FALSE parameter and manually refresh the stage metadata after each file is uploaded. This gives more control over which files are processed.
E. Adjust the file format definition associated with the stage to be more permissive, allowing for variations in the JSON structure. For example, use 'STRIP OUTER ARRAY = TRUE and configure error handling within the file format.

Answer: B

Explanation:
The best approach is to use a Snowpark Python UDF (option C) to pre-process and cleanse the JSON files. This allows for handling schema variations and correcting invalid JSON structures before loading them into Snowflake. 'ON ERROR = SKIP FILE might skip important data without proper investigation. A landing stage (option B) adds complexity and requires additional automation. Making the file format too permissive (option D) may lead to incorrect data loading. Disabling auto-refresh (option E) defeats the purpose of a continuous data pipeline.

NEW QUESTION # 53
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. 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.
B. 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
C. 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.
D. 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.
E. 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.

Answer: A,D

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 # 54
You are designing a data pipeline that involves unloading large amounts of data (hundreds of terabytes) from Snowflake to AWS S3 for archival purposes. To optimize cost and performance, which of the following strategies should you consider? (Select ALL that apply)

A. Use a large Snowflake warehouse size to parallelize the unload operation and reduce the overall unload time.
B. Utilize the 'MAX FILE SIZE parameter in the 'COPY INTO' command to control the size of individual files unloaded to S3. Smaller files generally improve query performance in S3.
C. Enable client-side encryption with KMS in S3 and specify the encryption key in the 'COPY INTO' command to enhance security.
D. Choose a file format such as Parquet or ORC with compression enabled to reduce storage costs and improve query performance in S3.
E. Partition the data during the unload operation based on a high-cardinality column to maximize parallelism in S3.

Answer: A,C,D

Explanation:
Using a larger warehouse size allows Snowflake to parallelize the unload operation, reducing the time it takes to unload large datasets. Enabling client-side encryption with KMS ensures that the data is encrypted both in transit and at rest in S3, enhancing security. Choosing a columnar file format like Parquet or ORC with compression significantly reduces storage costs and improves query performance when the data is later accessed in S3. Partitioning based on a high-cardinality column can lead to a large number of small files, which can negatively impact query performance in S3. While 'MAX FILE_SIZE is useful, smaller files don't always improve query performance and can even be detrimental.

NEW QUESTION # 55
......

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