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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q74-Q79):

NEW QUESTION # 74
A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data.
The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort.
Which solution will meet these requirements with the LEAST operational overhead?

A. AWS Step Functions tasks
B. AWS Lambda functions
C. AWS Glue workflows
D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows

Answer: C

Explanation:
AWS Glue workflows are a feature of AWS Glue that enable you to create and visualize complex ETL pipelines using AWS Glue components, such as crawlers, jobs, triggers, and development endpoints. AWS Glue workflows provide automated orchestration and require minimal manual effort, as they handle dependency resolution, error handling, state management, and resource allocation for your ETL workflows.
You can use AWS Glue workflows to ingest data from your operational databases into your Amazon S3 based data lake, and then use AWS Glue and Amazon EMR to process the data in the data lake. This solution will meet the requirements with the least operational overhead, as it leverages the serverless and fully managed nature of AWS Glue, and the scalability and flexibility of Amazon EMR12.
The other options are not optimal for the following reasons:
* B. AWS Step Functions tasks. AWS Step Functions is a service that lets you coordinate multiple AWS services into serverless workflows. You can use AWS Step Functions tasks to invoke AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use AWS Step Functions state machines to define the logic and flow of your workflows. However, this option would require more manual effort than AWS Glue workflows, as you would need to write JSON code to define your state machines, handle errors and retries, and monitor the execution history and status of your workflows3.
* C. AWS Lambda functions. AWS Lambda is a service that lets you run code without provisioning or managing servers. You can use AWS Lambda functions to trigger AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use AWS Lambda event sources and destinations to orchestrate the flow of your workflows. However, this option would also require more manual effort than AWS Glue workflows, as you would need to write code to implement your business logic, handle errors and retries, and monitor the invocation and execution of your Lambda functions. Moreover, AWS Lambda functions have limitations on the execution time, memory, and concurrency, which may affect the performance and scalability of your ETL workflows.
* D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows. Amazon MWAA is a managed service that makes it easy to run open source Apache Airflow on AWS. Apache Airflow is a popular tool for creating and managing complex ETL pipelines using directed acyclic graphs (DAGs).
You can use Amazon MWAA workflows to orchestrate AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use the Airflow web interface to visualize and monitor your workflows.
However, this option would have more operational overhead than AWS Glue workflows, as you would need to set up and configure your Amazon MWAA environment, write Python code to define your DAGs, and manage the dependencies and versions of your Airflow plugins and operators.
References:
* 1: AWS Glue Workflows
* 2: AWS Glue and Amazon EMR
* 3: AWS Step Functions
* : AWS Lambda
* : Amazon Managed Workflows for Apache Airflow

NEW QUESTION # 75
A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The .csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the dataset. The data analysts rarely query the entire file.
Which solution will meet these requirements MOST cost-effectively?

A. Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.
B. Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.
C. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to ingest the data into the data lake in JSON format.
D. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to write the data into the data lake in Apache Parquet format.

Answer: D

Explanation:
Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. Athena supports various data formats, such as CSV, JSON, ORC, Avro, and Parquet. However, not all data formats are equally efficient for querying. Some data formats, such as CSV and JSON, are row-oriented, meaning that they store data as a sequence of records, each with the same fields. Row-oriented formats are suitable for loading and exporting data, but they are not optimal for analytical queries that often access only a subset of columns. Row-oriented formats also do not support compression or encoding techniques that can reduce the data size and improve the query performance.
On the other hand, some data formats, such as ORC and Parquet, are column-oriented, meaning that they store data as a collection of columns, each with a specific data type. Column-oriented formats are ideal for analytical queries that often filter, aggregate, or join data by columns. Column-oriented formats also support compression and encoding techniques that can reduce the data size and improve the query performance. For example, Parquet supports dictionary encoding, which replaces repeated values with numeric codes, and run-length encoding, which replaces consecutive identical values with a single value and a count. Parquet also supports various compression algorithms, such as Snappy, GZIP, and ZSTD, that can further reduce the data size and improve the query performance.
Therefore, creating an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source and writing the data into the data lake in Apache Parquet format will meet the requirements most cost-effectively. AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue ETL jobs allow you to transform and load data from various sources into various targets, using either a graphical interface (AWS Glue Studio) or a code-based interface (AWS Glue console or AWS Glue API). By using AWS Glue ETL jobs, you can easily convert the data from CSV to Parquet format, without having to write or manage any code. Parquet is a column-oriented format that allows Athena to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. This solution will also reduce the cost of Athena queries, as Athena charges based on the amount of data scanned from S3.
The other options are not as cost-effective as creating an AWS Glue ETL job to write the data into the data lake in Parquet format. Using an AWS Glue PySpark job to ingest the source data into the data lake in .csv format will not improve the query performance or reduce the query cost, as .csv is a row-oriented format that does not support columnar access or compression. Creating an AWS Glue ETL job to ingest the data into the data lake in JSON format will not improve the query performance or reduce the query cost, as JSON is also a row-oriented format that does not support columnar access or compression. Using an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format will improve the query performance, as Avro is a column-oriented format that supports compression and encoding, but it will require more operational effort, as you will need to write and maintain PySpark code to convert the data from CSV to Avro format. Reference:
Amazon Athena
Choosing the Right Data Format
AWS Glue
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena

NEW QUESTION # 76
A company uses Amazon S3 to store data and Amazon QuickSight to create visualizations.
The company has an S3 bucket in an AWS account named Hub-Account. The S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The company's QuickSight instance is in a separate account named BI-Account The company updates the S3 bucket policy to grant access to the QuickSight service role. The company wants to enable cross-account access to allow QuickSight to interact with the S3 bucket.
Which combination of steps will meet this requirement? (Select TWO.)

A. Add the KMS key as a resource that the QuickSight service role can access.
B. Add the 53 bucket as a resource that the QuickSight service role can access.
C. Use the existing AWS KMS key to encrypt connections from QuickSight to the S3 bucket.
D. Use AWS Resource Access Manager (AWS RAM) to share the S3 bucket with the Bl-Account account.
E. Add an IAM policy to the QuickSight service role to give QuickSight access to the KMS key that encrypts the S3 bucket.

Answer: A,E

Explanation:
Problem Analysis:
The company needs cross-account access to allow QuickSight in BI-Account to interact with an S3 bucket in Hub-Account.
The bucket is encrypted with an AWS KMS key.
Appropriate permissions must be set for both S3 access and KMS decryption.
Key Considerations:
QuickSight requires IAM permissions to access S3 data and decrypt files using the KMS key.
Both S3 and KMS permissions need to be properly configured across accounts.
Solution Analysis:
Option A: Use Existing KMS Key for Encryption
While the existing KMS key is used for encryption, it must also grant decryption permissions to QuickSight.
Option B: Add S3 Bucket to QuickSight Role
Granting S3 bucket access to the QuickSight service role is necessary for cross-account access.
Option C: AWS RAM for Bucket Sharing
AWS RAM is not required; bucket policies and IAM roles suffice for granting cross-account access.
Option D: IAM Policy for KMS Access
QuickSight's service role in BI-Account needs explicit permissions to use the KMS key for decryption.
Option E: Add KMS Key as Resource for Role
The KMS key must explicitly list the QuickSight role as an entity that can access it.
Implementation Steps:
S3 Bucket Policy in Hub-Account:
Add a policy to the S3 bucket granting the QuickSight service role access:
json
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": { "AWS": "arn:aws:iam::<BI-Account-ID>:role/service-role/QuickSightRole" },
"Action": "s3:GetObject",
"Resource": "arn:aws:s3:::<Bucket-Name>/*"
}
]
}
KMS Key Policy in Hub-Account:
Add permissions for the QuickSight role:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": { "AWS": "arn:aws:iam::<BI-Account-ID>:role/service-role/QuickSightRole" },
"Action": [
"kms:Decrypt",
"kms:DescribeKey"
],
"Resource": "*"
}
]
}
IAM Policy for QuickSight Role in BI-Account:
Attach the following policy to the QuickSight service role:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3:GetObject",
"kms:Decrypt"
],
"Resource": [
"arn:aws:s3:::<Bucket-Name>/*",
"arn:aws:kms:<region>:<Hub-Account-ID>:key/<KMS-Key-ID>"
]
}
]
}
Reference:
Setting Up Cross-Account S3 Access
AWS KMS Key Policy Examples
Amazon QuickSight Cross-Account Access

NEW QUESTION # 77
A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The
.csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the dataset. The data analysts rarely query the entire file.
Which solution will meet these requirements MOST cost-effectively?

A. Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.
B. Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.
C. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source.Configure the job to write the data into the data lake in Apache Parquet format.
D. Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source.
Configure the job to ingest the data into the data lake in JSON format.

Answer: C

Explanation:
Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. Athena supports various data formats, such as CSV,JSON, ORC, Avro, and Parquet. However, not all data formats are equally efficient for querying. Some data formats, such as CSV and JSON, are row-oriented, meaning that they store data as a sequence of records, each with the same fields. Row-oriented formats are suitable for loading and exporting data, but they are not optimal for analytical queries that often access only a subset of columns. Row-oriented formats also do not support compression or encoding techniques that can reduce the data size and improve the query performance.
On the other hand, some data formats, such as ORC and Parquet, are column-oriented, meaning that they store data as a collection of columns, each with a specific data type. Column-oriented formats are ideal for analytical queries that often filter, aggregate, or join data by columns. Column-oriented formats also support compression and encoding techniques that can reduce the data size and improve the query performance. For example, Parquet supports dictionary encoding, which replaces repeated values with numeric codes, and run-length encoding, which replaces consecutive identical values with a single value and a count. Parquet also supports various compression algorithms, such as Snappy, GZIP, and ZSTD, that can further reduce the data size and improve the query performance.
Therefore, creating an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source and writing the data into the data lake in Apache Parquet format will meet the requirements most cost-effectively. AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue ETL jobs allow you to transform and load data from various sources into various targets, using either a graphical interface (AWS Glue Studio) or a code-based interface (AWS Glue console or AWS Glue API). By using AWS Glue ETL jobs, you can easily convert the data from CSV to Parquet format, without having to write or manage any code. Parquet is a column-oriented format that allows Athena to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. This solution will also reduce the cost of Athena queries, as Athena charges based on the amount of data scanned from S3.
The other options are not as cost-effective as creating an AWS Glue ETL job to write the data into the data lake in Parquet format. Using an AWS Glue PySpark job to ingest the source data into the data lake in .csv format will not improve the query performance or reduce the query cost, as .csv is a row-oriented format that does not support columnar access or compression. Creating an AWS Glue ETL job to ingest the data into the data lake in JSON format will not improve the query performance or reduce the query cost, as JSON is also a row-oriented format that does not support columnar access or compression. Using an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format will improve the query performance, as Avro is a column-oriented format that supports compression and encoding, but it will require more operational effort, as you will need to write and maintain PySpark code to convert the data from CSV to Avro format.
References:
Amazon Athena
Choosing the Right Data Format
AWS Glue
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena

NEW QUESTION # 78
A company is developing an application that runs on Amazon EC2 instances. Currently, the data that the application generates is temporary. However, the company needs to persist the data, even if the EC2 instances are terminated.
A data engineer must launch new EC2 instances from an Amazon Machine Image (AMI) and configure the instances to preserve the data.
Which solution will meet this requirement?

A. Launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data. Apply the default settings to the EC2 instances.
B. Launch new EC2 instances by using an AMI that is backed by a root Amazon Elastic Block Store (Amazon EBS) volume that contains the application data. Apply the default settings to the EC2 instances.
C. Launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume. Attach an Amazon Elastic Block Store (Amazon EBS) volume to contain the application data. Apply the default settings to the EC2 instances.
D. Launch new EC2 instances by using an AMI that is backed by an Amazon Elastic Block Store (Amazon EBS) volume. Attach an additional EC2 instance store volume to contain the application data. Apply the default settings to the EC2 instances.

Answer: C

Explanation:
Amazon EC2 instances can use two types of storage volumes: instance store volumes and Amazon EBS volumes. Instance store volumes are ephemeral, meaning they are only attached to the instance for the duration of its life cycle. If the instance is stopped, terminated, or fails, the data on the instance store volume is lost.
Amazon EBS volumes are persistent, meaning they can be detached from the instance and attached to another instance, and the data on the volume is preserved. To meet the requirement of persisting the data even if the EC2 instances are terminated, the data engineer must use Amazon EBS volumes to store the application data.
The solution is to launch new EC2 instances by using an AMI that is backed by an EC2 instance store volume, which is the default option for most AMIs. Then, the data engineer must attach an Amazon EBS volume to each instance and configure the application to write the data to the EBS volume. This way, the data will be saved on the EBS volume and can be accessed by another instance if needed. The data engineer can apply the default settings to the EC2 instances, as there is no need to modify the instance type, security group, or IAM role for this solution. The other options are either not feasible or not optimal. Launching new EC2 instances by using an AMI that is backed by an EC2 instance store volume that contains the application data (option A) or by using an AMI that is backed by a root Amazon EBS volume that contains the application data (option B) would not work, as the data on the AMI would be outdated and overwritten by the new instances. Attaching an additional EC2 instance store volume to contain the application data (option D)would not work, as the data on the instance store volume would be lost if the instance is terminated. References:
Amazon EC2 Instance Store
Amazon EBS Volumes
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.1: Amazon EC2

NEW QUESTION # 79
......

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q74-Q79):

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