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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
The JSON below is stored in a variant column named v in a table named jCustRaw:

Which query will return one row per team member (stored in the teamMembers array) along all of the attributes of each team member?

Answer: D

NEW QUESTION # 74
A data engineer set up an AWS Lambda function to read an object that is stored in an Amazon S3 bucket. The object is encrypted by an AWS KMS key.
The data engineer configured the Lambda function's execution role to access the S3 bucket.
However, the Lambda function encountered an error and failed to retrieve the content of the object.
What is the likely cause of the error?

A. The S3 bucket is located in a different AWS Region than the Region where the data engineer works. Latency issues caused the Lambda function to encounter an error.
B. The data engineer misconfigured the permissions of the S3 bucket. The Lambda function could not access the object.
C. The Lambda function's execution role does not have the necessary permissions to access the KMS key that can decrypt the S3 object.
D. The Lambda function is using an outdated SDK version, which caused the read failure.

Answer: C

NEW QUESTION # 75
A data engineer at a company is optimizing extract, transform, and load (ETL) workflows. The current architecture uses Amazon EMR and Apache Spark for large-scale transformations and AWS Glue for other ETL tasks. The workflows load processed data into an Amazon S3 based data lake.
The company wants to move to a fully managed serverless solution that can orchestrate multiple ETL jobs and automate execution. The new solution must continue to use Spark to process data.
The company needs to orchestrate and automate the ETL workflows with minimal manual intervention.
Which solution will meet these requirements?

A. Configure AWS Step Functions and Amazon EventBridge to orchestrate and invoke ETL workflows in AWS Glue and Amazon EMR.
B. Use Amazon Managed Workflows for Apache Airflow automatic scheduling to orchestrate the Spark-based ETL jobs.
C. Configure AWS Lambda functions to process Amazon S3 event notifications for data transformation tasks when new data is uploaded.
D. Migrate all ETL jobs to AWS Glue. Use AWS Glue workflows to orchestrate the pipeline.

Answer: D

Explanation:
AWS Glue provides a fully managed serverless ETL service with native Spark support. By migrating all ETL jobs to Glue, the company eliminates the need to manage Amazon EMR clusters. Glue workflows can orchestrate and automate multiple ETL jobs, ensuring dependencies are respected and executions are automated with minimal manual intervention. This satisfies the requirement for a serverless, Spark-based, fully managed solution.

NEW QUESTION # 76
A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue.
The data engineer's original query is as follows:
SELECT product_name, sum(sales_amount)
FROM sales_data
WHERE year = 2023
GROUP BY product_name
How should the data engineer modify the Athena query to meet these requirements?

A. Replace sum(sales_amount) with count(*) for the aggregation.
B. Change WHERE year = 2023 to WHERE extract(year FROM sales_data) = 2023.
C. Add HAVING sum(sales_amount) > 0 after the GROUP BY clause.
D. Remove the GROUP BY clause.

Answer: C

Explanation:
https://docs.aws.amazon.com/athena/latest/ug/select.html

NEW QUESTION # 77
An ecommerce company uses AWS Glue ETL to process and analyze orders. The company wants to build an extract, transform, and load (ETL) pipeline that processes placed, shipped, delivered, and canceled orders differently.
The company integrates the order processing system with Amazon EventBridge. The company configures EventBridge Scheduler rules for each order status to invoke different AWS Glue workflows. When the company examines Amazon CloudWatch metrics for the workflow, the company notices that the FailedInvocations metric shows a high value for canceled orders.
The company must determine the cause of the failed invocations.
Which solution will meet this requirement?

A. Configure a dead-letter queue in EventBridge Scheduler to store failed events. Analyze the failed order events.
B. Use the archive and replay features in EventBridge Scheduler to investigate the issue.
C. Change the retry policy in EventBridge Scheduler to reduce the value for maximum retries.
D. Change the retry policy in EventBridge Scheduler to increase the value for maximum age of event.

Answer: A

Explanation:
Configuring a dead-letter queue for EventBridge Scheduler captures the events that could not be delivered to the target along with the failure context. Reviewing the failed canceled-order events in the DLQ provides the most direct way to identify why the invocations are failing (for example, payload issues, permission errors, or target configuration problems) without changing retry behavior.

NEW QUESTION # 78
......

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