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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Data Transformation: The SnowPro Advanced: Data Engineer exam evaluates skills in using User-Defined Functions (UDFs), external functions, and stored procedures. It assesses the ability to handle semi-structured data and utilize Snowpark for transformations. This section ensures Snowflake engineers can effectively transform data within Snowflake environments, critical for data manipulation tasks.
Topic 2	Data Movement: Snowflake Data Engineers and Software Engineers are assessed on their proficiency to load, ingest, and troubleshoot data in Snowflake. It evaluates skills in building continuous data pipelines, configuring connectors, and designing data sharing solutions.
Topic 3	Security: The Security topic of the DEA-C01 test covers the principles of Snowflake security, including the management of system roles and data governance. It measures the ability to secure data and ensure compliance with policies, crucial for maintaining secure data environments for Snowflake Data Engineers and Software Engineers.
Topic 4	Storage and Data Protection: The topic tests the implementation of data recovery features and the understanding of Snowflake's Time Travel and micro-partitions. Engineers are evaluated on their ability to create new environments through cloning and ensure data protection, highlighting essential skills for maintaining Snowflake data integrity and accessibility.
Topic 5	Performance Optimization: This topic assesses the ability to optimize and troubleshoot underperforming queries in Snowflake. Candidates must demonstrate knowledge in configuring optimal solutions, utilizing caching, and monitoring data pipelines. It focuses on ensuring engineers can enhance performance based on specific scenarios, crucial for Snowflake Data Engineers and Software Engineers.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q147-Q152):

NEW QUESTION # 147
A company uses AWS Step Functions to orchestrate a data pipeline. The pipeline consists of Amazon EMR jobs that ingest data from data sources and store the data in an Amazon S3 bucket. The pipeline also includes EMR jobs that load the data to Amazon Redshift.
The company's cloud infrastructure team manually built a Step Functions state machine. The cloud infrastructure team launched an EMR cluster into a VPC to support the EMR jobs.
However, the deployed Step Functions state machine is not able to run the EMR jobs.
Which combination of steps should the company take to identify the reason the Step Functions state machine is not able to run the EMR jobs? (Choose two.)

A. Use AWS CloudFormation to automate the Step Functions state machine deployment. Create a step to pause the state machine during the EMR jobs that fail. Configure the step to wait for a human user to send approval through an email message. Include details of the EMR task in the email message for further analysis.
B. Query the flow logs for the VPC. Determine whether the traffic that originates from the EMR cluster can successfully reach the data providers. Determine whether any security group that might be attached to the Amazon EMR cluster allows connections to the data source servers on the informed ports.
C. Verify that the Step Functions state machine code has all IAM permissions that are necessary to create and run the EMR jobs. Verify that the Step Functions state machine code also includes IAM permissions to access the Amazon S3 buckets that the EMR jobs use. Use Access Analyzer for S3 to check the S3 access properties.
D. Check for entries in Amazon CloudWatch for the newly created EMR cluster. Change the AWS Step Functions state machine code to use Amazon EMR on EKS. Change the IAM access policies and the security group configuration for the Step Functions state machine code to reflect inclusion of Amazon Elastic Kubernetes Service (Amazon EKS).
E. Check the retry scenarios that the company configured for the EMR jobs. Increase the number of seconds in the interval between each EMR task. Validate that each fallback state has the appropriate catch for each decision state. Configure an Amazon Simple Notification Service (Amazon SNS) topic to store the error messages.

Answer: B,C

Explanation:
https://docs.aws.amazon.com/step-functions/latest/dg/procedure-create-iam-role.html
https://docs.aws.amazon.com/step-functions/latest/dg/service-integration-iam-templates.html

NEW QUESTION # 148
A data engineer is building a data pipeline. A large data file is uploaded to an Amazon S3 bucket once each day at unpredictable times. An AWS Glue workflow uses hundreds of workers to process the file and load the data into Amazon Redshift. The company wants to process the file as quickly as possible.
Which solution will meet these requirements?

A. Create an on-demand AWS Glue trigger to start the workflow. Create an AWS Database Migration Service (AWS DMS) migration task. Set the DMS source as the S3 bucket. Set the target endpoint as the AWS Glue workflow.
B. Create an event-based AWS Glue trigger to start the workflow. Configure Amazon S3 to log events to AWS CloudTrail. Create a rule in Amazon EventBridge to forward PutObject events to the AWS Glue trigger.
C. Create an on-demand AWS Glue trigger to start the workflow. Create an AWS Lambda function that runs every 15 minutes to check the S3 bucket for the daily file. Configure the function to start the AWS Glue workflow if the file is present.
D. Create a scheduled AWS Glue trigger to start the workflow. Create a cron job that runs the AWS Glue job every 15 minutes. Set up the AWS Glue job to check the S3 bucket for the daily file.
Configure the job to stop if the file is not present.

Answer: B

Explanation:
An event-based trigger driven by Amazon EventBridge provides the lowest-latency, lowest- overhead way to kick off your Glue workflow as soon as the file arrives. By enabling S3 data events in CloudTrail and writing a simple EventBridge rule that matches the PutObject event for your bucket, you can target your Glue workflow's trigger directly, eliminating continuous polling or extra Lambda or DMS infrastructure.

NEW QUESTION # 149
A data engineer needs to design a data pipeline that invokes an AWS Glue job. After the AWS Glue job finishes successfully, the pipeline needs to invoke three AWS Lambda functions. The pipeline must be serverless. The data engineer wants to see the entire pipeline lineage in a single interface.
Which solution will meet these requirements?

A. Deploy a workflow in AWS Step Functions to invoke the AWS Glue job. In the job code, invoke the Lambda functions before the job finishes. View the lineage from the AWS Glue UI.
B. Deploy an Apache Airflow workflow that invokes the AWS Glue job and the Lambda functions.
View the lineage in the Airflow UI.
C. Build the pipeline in the AWS Glue job. Invoke the Lambda functions after the AWS Glue job runs.
Use Amazon CloudWatch Logs Insights to view the lineage.
D. Configure a workflow in AWS Step Functions that invokes the AWS Glue job and the Lambda functions. View the lineage in Step Functions Workflow Studio.

Answer: D

Explanation:
AWS Step Functions is a fully serverless orchestration service that can coordinate an AWS Glue job and then invoke multiple Lambda functions after the job succeeds. It provides a single visual interface for the full workflow, making it easy to trace each step of the pipeline lineage in one place.

NEW QUESTION # 150
Two data engineering teams use separate AWS accounts. Both teams request access to the same datashare in an Amazon Redshift cluster that is in a third AWS account. The datashare is named salesshare.
A data engineer must use the Amazon Redshift SQL interface to grant both data engineering teams' access to the datashare.
Which command or commands will meet this requirement?

A. GRANT USAGE ON DATASHARE salesshare TO NAMESPACES '<account ID 1>' AND
'<account ID 2>';
B. GRANT USAGE ON DATASHARE salesshare TO ACCOUNTS '<account ID 1>' AND '<account ID 2>';
C. GRANT USAGE ON DATASHARE salesshare TO ACCOUNT '<account ID 1>';
GRANT USAGE ON DATASHARE salesshare TO ACCOUNT '<account ID 2>';
D. GRANT USAGE ON DATASHARE salesshare TO NAMESPACE '<account ID 1>';
GRANT USAGE ON DATASHARE salesshare TO NAMESPACE '<account ID 2>';

Answer: D

Explanation:
In Amazon Redshift data sharing, cross-account permissions for a datashare are granted to consumer cluster/serverless namespaces. Granting USAGE on the datashare to each consumer namespace enables both teams' Redshift environments in their respective accounts to create and query databases from the shared data.

NEW QUESTION # 151
As a skilled data engineer, you have been tasked with managing the Spark ETL jobs that run on the EMR and store incremental data in S3. However, you have noticed that some jobs are failing, and you are eager to take proactive steps to troubleshoot the issue. What would be the primary course of action to resolve this problem and ensure the smooth operation of the ETL process?

A. Restart the EMR cluster.
B. Check the available disk space on the HDFS cluster.
C. Increase the number of EC2 instances in the cluster.
D. Review the Spark job logs for errors or warnings.

Answer: D

Explanation:
Reviewing Spark job logs is the first step to identify errors, warnings, or slow stages that indicate the root cause of the performance issue.

NEW QUESTION # 152
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