DEA-C02 PASS4SURE GUIDE & DEA-C02 EXAM PREPARATION & DEA-C02 STUDY MATERIALS

DEA-C02 Pass4sure Guide & DEA-C02 Exam Preparation & DEA-C02 Study Materials

DEA-C02 Pass4sure Guide & DEA-C02 Exam Preparation & DEA-C02 Study Materials

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For Snowflake aspirants wishing to clear the Snowflake test and become a SnowPro Advanced: Data Engineer (DEA-C02) certification holder, BootcampPDF Snowflake DEA-C02 practice material is an excellent resource. By preparing with BootcampPDF actual Snowflake DEA-C02 Exam Questions, you can take get success on first attempt and take an important step toward accelerating your career. Download updated DEA-C02 exam questions today and start preparation.

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q128-Q133):

NEW QUESTION # 128
You're building a data pipeline that ingests JSON data from URLs representing real-time weather information. The data structure varies slightly between different weather providers, but all contain a 'location' object with 'city' and 'country' fields, and a 'temperature' field. You need to create a generic function that can handle these variations and extract the location and temperature, returning a flattened JSON object with keys 'city', 'country', and 'temperature'. You want to avoid explicit schema definition and take advantage of Snowflake's VARIANT data type flexibility Given the following sample JSON structures, which approach will best accomplish this?

  • A. Define a Snowflake stored procedure that uses 'SYSTEM$URL_GET to fetch the JSON data, then uses conditional logic with 'TRY TO BOOLEANS and STRY TO DATE to handle different data types. The stored procedure constructs a new JSON object with 'city', 'country', and 'temperature' fields using 'OBJECT_CONSTRUCT.
  • B. Define a Snowflake view that selects from a table containing the URLs, using 'SYSTEM$URL GET to fetch the JSON data and to extract the 'city', 'country', and 'temperature' fields. Use 'TRY_CAST to convert the 'temperature' to a numeric type.
  • C. Create a pipe that uses 'COPY INTO to ingest JSON data directly from the URLs into a VARIANT column. The 'FILE FORMAT object is configured to use = TRUE to handle different data types. Post ingestion create a view to query data.
  • D. Define a Snowflake external function (UDF) that fetches the JSON data using a Python library like 'requests' or The function then parses the JSON and extracts the required fields, handling potential missing fields using 'try...except' blocks. The function returns a JSON string representing the flattened object.
  • E. Create a Snowflake external function written in Java that uses 'java.net.lJRL' to fetch the JSON data and 'com.fasterxml.jackson.databind' library to parse it. Use Jackson's 'JsonNode' to navigate the varying JSON structure and extract 'city', 'country', and 'temperature' fields. Return a JSON string of the result.

Answer: D,E

Explanation:
Option B is the most flexible and robust. External functions allow leveraging powerful scripting languages (like Python) for parsing and manipulating JSON data, handling variations gracefully. Option E is similarly valid, using Java and Jackson, which gives similar control and flexibility. Option A is less desirable due to the complexity of handling different data types and missing fields directly within SQL. Option C is limited because it relies on predefined paths and doesn't easily handle variations in the JSON structure. Option D is not suitable since 'COPY INTO does not directly support URLs.


NEW QUESTION # 129
You're loading data into a Snowflake table using 'COPY INTO'. You notice that some rows are being rejected due to data validation errors (e.g., data type mismatch, uniqueness constraint violations). You want to implement a strategy to capture these rejected rows for further analysis and correction. Which of the following approaches offers the MOST efficient and reliable method for capturing and storing the rejected rows, minimizing performance impact during the data loading process? Assume no staging table exists before loading data to production table.

  • A. Option A
  • B. Option D
  • C. Option B
  • D. Option E
  • E. Option C

Answer: D

Explanation:
Option E, utilizing 'ERROR INTEGRATION', is the most efficient and reliable. It automatically captures rejected rows during the 'COPY INTO' process and stores them in a designated error table or stage, minimizing performance impact and providing a structured way to analyze and correct errors. Options A, B, C, and D have drawbacks. A requires pre-validation, adding overhead. B uses sampling, which might not identify all errors. C only provides a record count, not the actual rejected rows. D aborts the entire statement, impacting availability.


NEW QUESTION # 130
You are tasked with building a data pipeline that ingests customer interaction data from multiple microservices using Snowpipe Streaming. Each microservice writes data in JSON format to its own Kafka topic. You need to design an efficient and scalable solution to ingest this data into a single Snowflake table, while ensuring data integrity and minimizing latency. Consider these constraints: 1. High data volume with variable ingestion rates. 2. The need to correlate data from different microservices based on a common 'customer id'. 3. Potential for schema evolution in the microservices. Given these requirements and constraints, which of the following architectural approaches, leveraging Snowpipe Streaming features and Snowflake capabilities, would be the MOST appropriate and robust?

  • A. Implement a custom Kafka Connect connector that directly writes data to Snowflake using Snowpipe Streaming. The connector should handle schema evolution and routing based on topic name. Define a clustering key on the Snowflake table on the 'customer id'
  • B. Create a separate Snowpipe Streaming client for each Kafka topic, ingesting data into separate staging tables. Then, use a scheduled task to merge the data into the final target table based on 'customer id'.
  • C. Use a single Snowpipe Streaming client to ingest data from all Kafka topics into a single VARIANT column in the Snowflake table. Then, use Snowflake's external functions to transform and load the data into the final target table based on the 'customer_id'
  • D. Develop a Spark Streaming application that reads data from Kafka, transforms it, and then uses the Snowflake Connector for Spark to write the data to Snowflake in micro-batches.
  • E. Develop a single Snowpipe Streaming client that consumes data from all Kafka topics, using a transformation function to route the data to the correct table based on the topic name. Use Snowflake's clustering key on 'customer _ id' for efficient querying.

Answer: A

Explanation:
D is the MOST appropriate solution. A custom Kafka Connect connector provides the most robust and scalable approach. It can handle consuming from multiple topics, manage schema evolution, and use Snowpipe Streaming directly for ingestion. A is less efficient due to multiple Snowpipe clients and scheduled merging. B is difficult to maintain with schema evolution and routing using transformation functions. C is inefficient due to post-ingestion transformation. E, while viable, introduces the complexity and overhead of Spark when Snowpipe Streaming offers a more direct solution.


NEW QUESTION # 131
You are working with a Snowpark DataFrame named 'customer data' that contains sensitive Personally Identifiable Information (PII). The DataFrame has columns such as 'customer id', 'name', 'email' , and 'phone number'. Your task is to create a new DataFrame that only contains 'customer id' and a hash of the 'email' address for anonymization purposes, while also filtering out any customers whose 'customer id' starts with 'TEMP'. Which of the following approaches adheres to best practices for data security and efficiency in Snowpark, using secure hashing algorithms provided by Snowflake?

  • A. Option E
  • B. Option A
  • C. Option D
  • D. Option B
  • E. Option C

Answer: C

Explanation:
Option D is the most appropriate. 'sha2 with a bit length of 256 or higher (like 256 in this example) is a strong cryptographic hash function suitable for anonymizing sensitive data. The 'where' function is used with the negation of the 'startswith' function (through column reference 'col()'), so it appropriately filters out customer IDs starting with 'TEMP. Using 'select' projects only the necessary columns, minimizing the risk of exposing other PII data. Option A utilizes the 'filter' and provides the correct filter. Option C attempts to utilize However, cache_result() is not suitable for this task. Option B, however, is suboptimal because MD5 is considered cryptographically broken and should not be used for security-sensitive applications. Options A and E are technically correct in filtering out customer IDs. They are not as clear as Option D. The code will accomplish the objective of the question but not clearly show which customer IDs will be retained.


NEW QUESTION # 132
Which of the following statements are accurate regarding the differences between SQL UDFs and Java UDFs in Snowflake? (Select two)

  • A. Java UDFs always execute faster than SQL UDFs due to JVM optimizations.
  • B. SQL UDFs are defined using SQL code within Snowflake, whereas Java UDFs require uploading a JAR file containing the compiled Java code.
  • C. Java UDFs are deprecated and should not be used; instead, SQL UDFs are recommended for all scenarios.
  • D. SQL UDFs can only be used for simple transformations and cannot execute external calls, while Java UDFs can perform complex logic and interact with external services via libraries.
  • E. SQL UDFs and Java UDFs are interchangeable, and there is no performance difference between them.

Answer: B,D

Explanation:
SQL UDFs are suitable for simpler transformations within Snowflake and cannot make external calls. They are defined directly using SQL code. Java UDFs, on the other hand, offer more flexibility by allowing complex logic implementation, interaction with external services/libraries via JAR files, and custom code. Java UDFs are generally perform better when complex transformations are needed, where SQL UDFs can become cumbersome. Performance depends on the workload. Option B is wrong becuase SQL UDFs are more performant for simpler tasks. Option D is wrong becuase its highly dependant on workload, where options E is wrong as Java UDFs are very useful and not deprecated.


NEW QUESTION # 133
......

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