FIRST-GRADE LATEST DEA-C02 TEST CRAM FOR REAL EXAM

First-grade Latest DEA-C02 Test Cram for Real Exam

First-grade Latest DEA-C02 Test Cram for Real Exam

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Tags: Latest DEA-C02 Test Cram, DEA-C02 Complete Exam Dumps, Valid DEA-C02 Study Guide, Latest DEA-C02 Test Questions, Reliable DEA-C02 Test Prep

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q282-Q287):

NEW QUESTION # 282
You are tasked with creating a Python script to load data from a CSV file stored in an AWS S3 bucket into a Snowflake table. You have the following requirements: 1. Use the 'COPY INTO' command for efficient data loading. 2. Handle potential schema evolution in the CSV file (e.g., new columns being added). 3. Automatically create the target table if it doesn't exist, inferring the schema from the CSV. Which combination of Snowflake Python connector functionalities and 'COPY INTO' options would best address these requirements, assuming you have an AWS IAM role configured for Snowflake access to S3?

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

Answer: B

Explanation:
Option C is the most effective solution. 'ON_ERROR = CONTINUE allows the 'COPY INTO' command to proceed even if it encounters errors (e.g., due to extra columns). = CASE INSENSITIVE' handles slight variations in column names between the CSV and the Snowflake table. Setting = TRUE in the Snowflake session (this is a hypothetical feature, Snowflake doesn't directly have AUTO_CREATE_TABLE in COPY INTO, so this tests knowledge of workarounds and understanding of COPY INTO capabilities). Other options have drawbacks. Option A doesn't directly use COPY INTO and is generally slower. Option B requires a predefined schema, negating the ability to handle schema evolution. Option D is used with in memory stream, rather s3. Option E involves manual schema inspection and construction, which is cumbersome and error-prone.


NEW QUESTION # 283
You are implementing a data share between two Snowflake accounts. The provider account wants to grant the consumer account access to a function that returns anonymized customer data based on a complex algorithm. The provider wants to ensure that the consumer cannot see the underlying implementation details of the anonymization algorithm. Which of the following approaches can achieve this goal? (Select TWO)

  • A. Share the underlying table and provide the consumer account with the anonymization algorithm separately.
  • B. Create an external function in the provider account and grant usage to the share. Share the share with the consumer account.
  • C. Create a secure UDF in the provider account and grant usage on the secure UDF to the share. Share the share with the consumer account.
  • D. Create a standard UDF in the provider account and grant usage on the UDF to the share. Share the share with the consumer account.
  • E. Create a view that calls the secure UDF and share that view with the consumer account.

Answer: C,E

Explanation:
A secure UDF hides the underlying implementation details from the consumer. Option 'A' achieves this directly. Creating a view (Option 'D') that calls the secure UDF provides another layer of abstraction, further protecting the algorithm's implementation. A standard UDF (Option B) does not hide the implementation. Sharing the table directly (Option C) defeats the purpose of anonymization. While external functions exist (Option E), they would be unnecessarily complex in this scenario, which can be achieved natively through secure UDF and View combination.


NEW QUESTION # 284
A data engineering team is implementing Row Access Policies (RAP) on a table 'employee_data' containing sensitive salary information. They need to ensure that only managers can see the salary information of their direct reports. A user-defined function (UDF) 'GET returns a comma-separated string of manager usernames for a given username. Which of the following SQL statements correctly creates and applies a RAP to achieve this?

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

Answer: A

Explanation:
Option D correctly uses EXISTS and SPLIT TO TABLE to check if the employee's username is present in the list of managers returned by the GET_MANAGERS UDE Options A, B, C, and E have logical errors in how they determine manager-employee relationships, or don't correctly handle the comma-separated string of managers returned by the UDF. Options A and C also use CURRENT ROLE() = 'MANAGER' which requires the user to explicitly set their role to 'MANAGER', which might not be practical.


NEW QUESTION # 285
You are tasked with designing a solution to ingest a continuous stream of unstructured log data from various sources into Snowflake. The log data includes text, JSON, and XML formats. The goal is to efficiently store the data, allow for flexible querying, and minimize storage costs. Which of the following approaches would BEST address these requirements? (Select TWO)

  • A. Ingest all log data into a single VARIANT column in a Snowflake table.
  • B. Use Snowflake's external functions to parse the log data during query execution.
  • C. Ingest all data 'as is' into a raw staging table. Then create a Task that use Python UDF to parse data and save it to different tables as required
  • D. Create separate tables for each log data format (text, JSON, XML).
  • E. Pre-process the log data to convert all formats into a standardized JSON format before ingestion.

Answer: A,B

Explanation:
Options A and D are the most effective. Storing all log data in a VARIANT column allows for flexibility in handling different formats. Using external functions during query execution allows for on-demand parsing and transformation, avoiding the need to pre-process or create multiple tables. Option B could be viable, but introduces overhead. Option C requires creating a lot of tables. Option E is more complex than it should be for generic use cases. Parsing during query execution with Snowflake's native features/External Functions in conjunction with variant is generally recommended.


NEW QUESTION # 286
You are designing a Snowpipe pipeline to ingest data from an AWS SQS queue. The queue contains notifications about new files arriving in an S3 bucket. However, due to network issues, some notifications are delayed, causing Snowpipe to potentially miss files. Which of the following strategies, when combined, will BEST address the problem of delayed notifications and ensure data completeness?

  • A. Increase the 'MAX FILE AGE parameter in the Snowpipe definition and implement a periodic 'ALTER PIPE ... REFRESH' command.
  • B. Configure the SQS queue with a longer retention period and implement an event bridge rule with a retry policy to resend notifications.
  • C. Use 'VALIDATE()' function periodically to identify files that have not been loaded and trigger manual data loads for missing data.
  • D. Implement a Lambda function that triggers the 'SYSTEM$PIPE FORCE RESUME procedure after a certain delay.
  • E. Set 'MAX FILE_AGE to 'DEFAULT' and utilize the 'SYSTEM$PIPE FORCE RESUME' procedure in conjunction with a separate process that lists the S3 bucket and compares it to the files already loaded in Snowflake, loading any missing files.

Answer: E

Explanation:
Option E provides the most robust solution. Setting 'MAX FILE AGE to 'DEFAULT' ensures that Snowpipe considers all files, regardless of their age. 'SYSTEM$PIPE FORCE RESUME' can help in some cases. The key component is the supplemental process that actively compares the S3 bucket contents with the loaded data, identifying and loading any missing files due to delayed notifications. This approach guarantees data completeness even with delayed or missed SQS notifications. A,B,C,D doesn't guarantee data completeness. A alone can cause huge latency issue. B is not optimal. C addresses the SQS issue but not guaranteed to catch every case. D requires manual load intervention


NEW QUESTION # 287
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