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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q105-Q110):

NEW QUESTION # 105
A daily process loads data into a Snowflake table named 'TRANSACTIONS using a COPY INTO statement. The table is clustered on 'TRANSACTION DATE'. Over time, you observe a significant degradation in query performance when querying data within specific date ranges. Analyzing the 'SYSTEM$CLUSTERING INFORMATION' function output for the 'TRANSACTIONS' table reveals a low 'effective clustering_ratio' and a high 'average_overlaps'. Which combination of actions below would BEST address the performance degradation and improve query efficiency?

A. Recluster the table using 'ALTER TABLE TRANSACTIONS RECLUSTER$ and adjust the virtual warehouse size to maximize resource allocation during the recluster operation.
B. Implement a data maintenance schedule that regularly reclusters the table using 'ALTER TABLE TRANSACTIONS RECLUSTER;' during off-peak hours and monitor the 'SYSTEM$CLUSTERING INFORMATION' function periodically.
C. Create a new table with the desired clustering and load data using 'CREATE TABLE AS SELECT statement.
D. Drop the existing clustering key on 'TRANSACTION_DATE, then recreate it with a different clustering key such as 'HASH(TRANSACTION_ID)'.
E. Drop the current clustered table and create a new table with partition by clauses

Answer: A,B

Explanation:
A low 'effective_clustering_ratio' and high 'average_overlaps' indicate that the data is not well-clustered, leading to inefficient query performance. Reclustering the table (A) reorganizes the data based on the clustering key, improving clustering. Creating a schedule (D) ensures that the table remains well-clustered over time. Dropping the clustering key and recreating it with a hash on (B) is unlikely to improve performance for date-range queries. Creating new table via 'CREATE TABLE AS SELECT statement is not the right way. In addition, 'Partition By' clause doesn't exist in Snowflake.

NEW QUESTION # 106
You have a directory table 'my_directory_table' pointing to a stage containing CSV files with headers. You need to query the directory table to find all files modified in the last 24 hours and load those CSV files using COPY INTO into a target table Assume the target table exists and has appropriate schema'. Which of the following SQL statements, or set of statements, will accomplish this efficiently? Note: Consider efficient file loading.

Answer: A

Explanation:
Option E is the most efficient way to load the files using 'COPY INTO' and the 'FILES' parameter. The 'FILES' parameter accepts a list of filenames to load. Option D is incorrect because the 'PATTERN' parameter expects a regular expression, not a concatenation of filenames. Options A, B, and C require more complex processing. Using subquery expression in the copy command is the best approach for filtering files based on directory table data.

NEW QUESTION # 107
You have a requirement to create a UDF in Snowflake that transforms data based on a complex set of rules defined in an external Python library. The library requires specific dependencies. You also need to ensure the UDF is secure and that the code is not visible to unauthorized users. Which of the following steps MUST be taken to achieve this?

A. Create a Snowflake Anaconda environment specifying the required Python library dependencies. Then, create a Python UDF, reference the Anaconda environment, and use the 'SECURE' keyword.
B. Upload the Python library and its dependencies as internal stages. Create a Java UDF that executes the Python code using the 'ProcessBuilder' class. Mark the Java UDF as 'SECURE'
C. Create an external function pointing to an AWS Lambda function or Azure Function that hosts the Python code and its dependencies. Secure the external function using API integration and role-based access control.
D. Create a Python UDF and directly upload the Python library code into the UDF's body. Snowflake automatically manages dependencies for UDFs.
E. Package all the Python libaries code into one file, then create an Javascript UDF and load/execute the python code inside the Javascript UDF.

Answer: A

Explanation:
Using Snowflake Anaconda environments allows you to manage Python dependencies for UDFs. Creating a Python UDF referencing the environment and using the 'SECURE keyword ensures both dependency management and code protection. Uploading libraries as internal stages and using Java UDFs is an unnecessarily complex approach. Snowflake does not automatically manage dependencies; they must be explicitly specified through Anaconda. Creating a Python inside a Javascript UDF is not a supported pattern

NEW QUESTION # 108
You are tasked with building a data pipeline that ingests JSON data from a series of publically accessible URLs. These URLs are provided as a list within a Snowflake table 'metadata_table', containing columns 'file_name' and 'file url'. Each JSON file contains information about products. You need to create a view that extracts product name, price, and a flag indicating whether the product description contains the word 'discount'. Which of the following approaches correctly implements this, optimizing for both performance and minimal code duplication, using external functions for text processing?

A. Create an external function that takes a URL as input and returns a BOOLEAN indicating if any error occured while processing the URL and the data. Create a stored procedure that iterates through 'metadata_table' , calls external function for each URL, reports error and then processes the data. A stage must also be created to host external function code.
B. Create an external function that takes a string as input and returns a BOOLEAN whether that string contains 'discount. Create a view on top of metadata_table', and using 'SYSTEM$URL_GET' fetch the content from 'file_url'. The JSON can then be parsed and the fields like price, name and description can be fetched. Use within the view to flag the presence of discount.
C. Create a pipe using 'COPY INTO' statement with 'FILE FORMAT = (TYPE = JSON)' and 'ON_ERROR = CONTINUE that loads the JSON files directly into a staging table. Create a view on top of the staging table to extract the required fields. The must have = TRUE' configured if JSON files are nested array. Use ' ILIKE in your view for the discount flag.
D. Create a stored procedure that iterates through 'metadata_table', downloads each JSON file using 'SYSTEM$URL GET, parses the JSON, extracts the required fields, and inserts the data into a target table. Then, create a view on top of the target table. Use 'LIKE '%discount%' to identify if a product description contains the word 'discount'.
E. Create an external function that takes a URL as input and returns a JSON variant containing the extracted product name, price, and discount flag (using 'LIKE Then, create a view that selects from calls the external function with 'SYSTEM$URL as input, and extracts the desired attributes from the returned JSON variant. A stage must also be created to host external function code.

Answer: B,E

Explanation:
Option B correctly leverages an external function to encapsulate the logic of fetching and processing the JSON data from the URL. The external function promotes code reusability and reduces complexity in the view. Option E is also correct as it demonstrates how to process fetched JSON data and use a UDF to enhance the transformation. Option A involves a procedural approach that is less efficient than using an external function or pipes. Option C does not directly work with data URLs and is geared more towards data residing within Snowflake storage. Option D is incorrect because it creates the external function just for identifying errors and creates a stored procedure just to process the data.

NEW QUESTION # 109
You accidentally truncated a large table named 'SALES DATA' in your 'REPORTING DB" database. You realize this happened 2 days ago, and your account has the default Time Travel retention of 1 day. You need to recover this table with minimal downtime. Analyze the situation and determine the best course of action, considering cost and recovery time.

A. Raise a support ticket requesting data recovery from failsafe. Since data retention period has expired.
B. Because the data retention period has expired, the table is unrecoverable using Snowflake's built-in features; you must restore from an external backup solution if available.
C. Immediately contact Snowflake Support to initiate a restore from Fail-safe, understanding that this process may take several hours or even days.
D. Increase the account-level to 2 days and then use the UNDROP TABLE SALES_DATA' command.
E. Create a clone of the table using the 'AT clause and a timestamp from 1 day ago. This would prevent any additional cost.

Answer: C

Explanation:
Since the Time Travel window (1 day) has passed, the 'UNDROP' command and cloning using or 'BEFORE clauses will not work (eliminating options B and D). While option C might be true if no external backups exist, the most appropriate first step is to contact Snowflake support (option A). This is because, even though Time Travel has expired, Fail-safe might still contain the data, offering a potential recovery path, although it is a longer process and not guaranteed. Option E is also a valid answer.

NEW QUESTION # 110
......

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Exam DEA-C02 Reviews & DEA-C02 Passing Score

DEA-C02 real exam dumps: SnowPro Advanced: Data Engineer (DEA-C02) & DEA-C02 free practice exam

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q105-Q110):

Valid DEA-C02 pdf vce & Snowflake DEA-C02 test answers & DEA-C02 troytec exams

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