Snowflake SnowPro Advanced: Data Engineer Certification - DEA-C01 FREE EXAM DUMPS QUESTIONS & ANSWERS]

Question 1

A company is setting up a data pipeline in AWS. The pipeline extracts client data from Amazon S3 buckets, performs quality checks, and transforms the data. The pipeline stores the processed data in a relational database. The company will use the processed data for future queries.
Which solution will meet these requirements MOST cost-effectively?

A. Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue DataBrew to perform quality checks.
Load the processed data and the quality check results into a new S3 bucket. B. Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data and quality check results into an Amazon RDS for MySQL instance. C. Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue Data Quality to enforce suggested quality rules. Load the data and the quality check results into an Amazon RDS for MySQL instance. D. Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data into an Amazon RDS for MySQL instance. Load the quality check results into a new S3 bucket.

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Correct Answer: C Vote an answer

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Question 2

A company stores objects in an Amazon S3 bucket. The company crawls the objects so that Amazon Athena can query the data.
A data engineer manually moved all objects from the partition with a path prefix of status=01 to the prefix status=02. The status=01 partition location is now empty. However, the status=01 partition location still appears in the AWS Glue Data Catalog metadata.
Which Athena command should the data engineer run to resolve the metadata discrepancy?

A. MSCK REPAIR TABLE B. ALTER TABLE SET TBLPROPERTIES C. ALTER TABLE CHANGE COLUMN D. ALTER TABLE DROP PARTITION

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Correct Answer: D Vote an answer

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Question 3

A company uses AWS Step Functions to orchestrate a data pipeline. The company has configured the Step Functions logs to push to Amazon CloudWatch Logs when the log level is FATAL The company has enabled logs for all AWS services in the pipeline.
A state named "preprocessing" invokes an AWS Lambda function named "preprocessing." The Lambda function preprocesses data before proceeding to the next state. The company needs to find error details if an error occurs during the data preprocessing.
Which CloudWatch Logs log group should the company check to find the error details?

A. The AWS CloudTrail logs SendTaskFailure event in the CloudTrail/logs/preprocessing log group B. The Step Functions TaskFailed event in the /aws/vendedlogs/states log group C. The Step Functions TaskSucceeded event in the /aws/vendedlogs/states log group D. The Lambda logs in the laws/lambda/preprocessing log group

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Question 4

A company wants to migrate a data warehouse from Teradata to Amazon Redshift.
Which solution will meet this requirement with the LEAST operational effort?

A. Use AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema.
Use AWS DMS to migrate the data. B. Use the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data. C. Manually export the schema definition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data. D. Use AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.

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Correct Answer: B Vote an answer

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Question 5

A company has a data processing pipeline that includes several dozen steps. The data processing pipeline needs to send alerts in real time when a step fails or succeeds. The data processing pipeline uses a combination of Amazon S3 buckets, AWS Lambda functions, and AWS Step Functions state machines.
A data engineer needs to create a solution to monitor the entire pipeline.
Which solution will meet these requirements?

A. Configure the Step Functions state machines to store notifications in an Amazon S3 bucket when the state machines finish running. Enable S3 event notifications on the S3 bucket. B. Use AWS CloudTrail to send a message to an Amazon Simple Notification Service (Amazon SNS) topic that sends notifications when a state machine fails to run or succeeds to run. C. figure the AWS Lambda functions to store notifications in an Amazon S3 bucket when the state machines finish running. Enable S3 event notifications on the S3 bucket. D. Configure an Amazon EventBridge rule to react when the execution status of a state machine changes. Configure the rule to send a message to an Amazon Simple Notification Service (Amazon SNS) topic that sends notifications.

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Question 6

A gaming company uses Amazon Kinesis Data Streams to collect clickstream data. The company uses Amazon Data Firehose delivery streams to store the data in JSON format in Amazon S3.
Data scientists at the company use Amazon Athena to query the most recent data to obtain business insights.
The company wants to reduce Athena costs but does not want to recreate the data pipeline.
Which solution will meet these requirements with the LEAST management effort?

A. Create an Apache Spark job that combines JSON files and converts the JSON files to Apache Parquet files. Launch an Amazon EMR ephemeral cluster every day to run the Spark job to create new Parquet files in a different S3 location. Use the ALTER TABLE SET LOCATION statement to reflect the new S3 location on the existing Athena table. B. Create a Kinesis data stream as a delivery destination for Firehose. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to run Apache Flink on the Kinesis data stream. Use Flink to aggregate the data and save the data to Amazon S3 in Apache Parquet format with a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table. C. Change the Firehose output format to Apache Parquet. Provide a custom S3 object YYYYMMDD prefix expression and specify a large buffer size.
For the existing data, create an AWS Glue extract, transform, and load (ETL) job. Configure the ETL job to combine small JSON files, convert the JSON files to large Parquet files, and add the YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table. D. Integrate an AWS Lambda function with Firehose to convert source records to Apache Parquet and write them to Amazon S3. In parallel, run an AWS Glue extract, transform, and load (ETL) job to combine the JSON files and convert the JSON files to large Parquet files. Create a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.

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Question 7

A manufacturing company is setting up an IoT monitoring system that generates large, complex data streams. The company wants to store the data in an Amazon S3 data lake for real-time and historical analysis. The company needs a solution that can process data quickly, provide short query times, and use resources efficiently without slowing down data ingestion.
The solution must use a Spark streaming extract, transform, and load (ETL) job on Amazon EMR that is configured to write data to an Iceberg table.
Which solution will meet these requirements?

A. Use Amazon Kinesis Data Streams to ingest the data. Configure the Iceberg table with copy on write (CoW) mode. Enable the AWS Glue Data Catalog compaction optimizer. B. Use Amazon Data Firehose to ingest the data. Use an AWS Lambda function to handle nested schema. Write the data to an Iceberg table with merge on read (MoR) mode in an Amazon S3 table bucket. C. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Configure the Iceberg table with copy on write (CoW) mode. Schedule an AWS Glue job for compaction to optimize the Iceberg table. D. Use Amazon Kinesis Data Streams to ingest the data. Configure the Iceberg table with merge on read (MoR) mode. Enable the AWS Glue Data Catalog compaction optimizer.

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Question 8

A company stores its processed data in an S3 bucket. The company has a strict data access policy. The company uses IAM roles to grant teams within the company different levels of access to the S3 bucket.
The company wants to receive notifications when a user violates the data access policy. Each notification must include the username of the user who violated the policy.
Which solution will meet these requirements?

A. Use Amazon S3 server access logs to monitor access to the bucket. Forward the access logs to an Amazon CloudWatch log group. Use metric filters on the log group to set up CloudWatch alarms. B. Use AWS Config rules to detect violations of the data access policy. Set up compliance alarms. C. Use Amazon CloudWatch metrics to gather object-level metrics. Set up CloudWatch alarms. D. Use AWS CloudTrail to track object-level events for the S3 bucket. Forward events to Amazon CloudWatch to set up CloudWatch alarms.

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Question 9

A company's application needs to search and analyze data in near real time. The application must handle up to 1,000 requests each second with low query latency. The company wants a solution that individual data teams can own and configure to meet each team's cost and performance optimization requirements. Which solution will meet these requirements?

A. Use streams in Amazon Kinesis Data Streams and Amazon Managed Service for Apache Flink to query and analyze the data. Assign each data team a separate stream to manage and consume. B. Use Amazon OpenSearch Service clusters with indexing to query the data. Assign each data team a separate cluster to configure for storage and queries. C. Use Amazon Aurora clusters that run on Aurora I/O-Optimized instances. Assign each data team a separate Aurora cluster to configure for storage and queries. D. Use Amazon S3 buckets to store the data. Use Amazon Athena to query and analyze the data.
Assign each data team a separate S3 bucket prefix to optimize queries.

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Question 10

A data engineer must orchestrate a data pipeline that consists of one AWS Lambda function and one AWS Glue job. The solution must integrate with AWS services.
Which solution will meet these requirements with the LEAST management overhead?

A. Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job. B. Use an Apache Airflow workflow that is deployed on Amazon Elastic Kubernetes Service (Amazon EKS). Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job. C. Use an AWS Step Functions workflow that includes a state machine. Configure the state machine to run the Lambda function and then the AWS Glue job. D. Use an Apache Airflow workflow that is deployed on an Amazon EC2 instance. Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.

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