Snowflake SnowPro Advanced: Data Engineer (DEA-C02) - DEA-C02 FREE EXAM DUMPS QUESTIONS & ANSWERS

A data engineering team has implemented a continuous data pipeline that loads data into a Snowflake table named 'SALES DATA' They notice that the pipeline intermittently experiences performance degradation, particularly during peak business hours. The team wants to implement alerts to proactively identify and address these performance issues. Which of the following approaches would be MOST effective for monitoring the pipeline and triggering alerts based on specific performance metrics related to data loading?

You are performing a series of complex data transformations on a large table named 'TRANSACTIONS' in Snowflake. After running several DML statements, you realize that an earlier transformation step introduced incorrect data into the table. You want to rollback the table to a state before that specific transformation occurred. Which of the following methods could be used to achieve this rollback, assuming you know the exact timestamp or query ID of the state you want to revert to? Select all that apply.

A Snowflake data pipeline ingests data from multiple external sources into a RAW DATA table. A transformation process then moves the data to a ANALYTICS DATA table, applying several complex UDFs written in Java and Python for data cleansing and enrichment. Performance is significantly slower than expected. Which combination of techniques would BEST improve the performance of this transformation pipeline?

You have data residing in AWS S3 in Parquet format, which is updated daily with new columns being added occasionally. The data is rarely accessed, but when it is, it needs to be queried using SQL within Snowflake. You want to minimize storage costs within Snowflake while ensuring the data can be queried without requiring manual table schema updates every time a new column is added to the S3 data'. Which approach is MOST suitable?

A data engineering team is using Snowflake's data lineage features, and they need to audit changes to data masking policies applied to a table named 'EMPLOYEES'. They want to identify when a masking policy was added, modified, or removed from specific columns.
What are the recommended Snowflake features or audit logs that the data engineering team could use to get these requirements?

A data engineer is using Snowpark Python to build a data pipeline. They need to define a UDF that uses a pre-trained machine learning model stored as a file in a Snowflake stage. The UDF should receive batches of data for scoring. Which of the following is the MOST efficient way to implement this, minimizing data transfer and execution time?

A data engineer is tasked with processing a large dataset of customer orders using Snowpark Python. The dataset contains a column stored as a string in 'YYYY-MM-DD HH:MI:SS' format. They need to create a new DataFrame with only the orders placed in the month of January 2023. Which of the following code snippets achieves this most efficiently, considering potential data volume and query performance?

You are tasked with migrating data from a legacy SQL Server database to Snowflake. One of the tables, 'ORDERS' , contains a column 'ORDER DETAILS that holds concatenated string data representing multiple order items. The data is formatted as 'iteml :qtyl ;item2:qty2;...'. You need to transform this string data into a JSON array of objects, where each object represents an item with 'name' and 'quantity' fields. Which of the following steps and functions would you use in Snowflake to achieve this transformation, in addition to loading the data?

You are tasked with creating a JavaScript UDF in Snowflake to parse JSON data containing nested arrays of objects. The UDF needs to extract specific values from these nested objects and return them as a comma-separated string. Given the JSON structure below, and the requirement to extract the 'value' field from each object within the 'items' array located inside each element of the 'data' array, which of the following JavaScript UDF definitions will correctly achieve this, assuming the input JSON is passed as a string?

A financial services company stores sensitive customer data, including credit card numbers, in a Snowflake table called 'CUSTOMER DATA. You need to implement dynamic data masking on the 'CREDIT CARD NUMBER column. You want to ensure that only users with the FINANCE ADMIN' role can view the unmasked credit card numbers. All other users should see a masked version of the data'. Which of the following set of commands is the MOST efficient and secure way to achieve this?