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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q128-Q133):

NEW QUESTION # 128
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. 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.
B. Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.
C. Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv 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: A

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 # 129
A data engineer notices slow query performance on a highly partitioned table that is in Amazon Athena. The table contains daily data for the previous 5 years, partitioned by date. The data engineer wants to improve query performance and to automate partition management. Which solution will meet these requirements?

A. Reduce the number of partitions by changing the partitioning schema from dairy to monthly granularity.
B. Use an AWS Lambda function that runs daily. Configure the function to manually create new partitions in AW5 Glue for each day's data.
C. Use partition projection in Athena. Configure the table properties by using a date range from 5 years ago to the present.
D. Increase the processing capacity of Athena queries by allocating more compute resources.

Answer: C

NEW QUESTION # 130
A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs.
The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.
Which solution will meet these requirements?

A. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.
B. Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.
C. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.
D. Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.

Answer: C

Explanation:
Amazon Kinesis Data Streams is a service that enables you to collect, process, and analyze real-time streaming data. You can use Kinesis Data Streams to ingest data from various sources, such as Amazon CloudWatch Logs, and deliver it to different destinations, such as Amazon S3 or Amazon Redshift. To use Kinesis Data Streams to deliver the security logs from the production AWS account to the security AWS account, you need to create a destination data stream in the security AWS account. This data stream will receive the log data from the CloudWatch Logs service in the production AWS account. To enable this cross- account data delivery, you need to create an IAM role and a trust policy in the security AWS account. The IAM role defines the permissions that the CloudWatch Logs service needs to put data into the destination data stream. The trust policy allows the production AWS account to assume the IAM role. Finally, you need to create a subscription filter in the production AWS account. A subscription filter defines the pattern to match log events and the destination to send the matching events. In this case, the destination is the destination data stream in the security AWS account. This solution meets the requirements of using Kinesis Data Streams to deliver the security logs to the security AWS account. The other options are either not possible or not optimal.
You cannot create a destination data stream in the production AWS account, as this would not deliver the data to the security AWS account. You cannot create a subscription filter in the security AWS account, as this would not capture the log events from the production AWS account. References:
* Using Amazon Kinesis Data Streams with Amazon CloudWatch Logs
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.3: Amazon Kinesis Data Streams

NEW QUESTION # 131
A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform.
The company wants to minimize the effort and time required to incorporate third-party datasets.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).
B. Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories.
C. Use API calls to access and integrate third-party datasets from AWS
D. Use API calls to access and integrate third-party datasets from AWS Data Exchange.

Answer: D

Explanation:
AWS Data Exchange is a service that makes it easy to find, subscribe to, and use third-party data in the cloud. It provides a secure and reliable way to access and integrate data from various sources, such as data providers, public datasets, or AWS services. Using AWS Data Exchange, you can browse and subscribe to data products that suit your needs, and then use API calls or the AWS Management Console to export the data to Amazon S3, where you can use it with your existing analytics platform. This solution minimizes the effort and time required to incorporate third-party datasets, as you do not need to set up and manage data pipelines, storage, or access controls. You also benefit from the data quality and freshness provided by the data providers, who can update their data products as frequently as needed12.
The other options are not optimal for the following reasons:
B . Use API calls to access and integrate third-party datasets from AWS. This option is vague and does not specify which AWS service or feature is used to access and integrate third-party datasets. AWS offers a variety of services and features that can help with data ingestion, processing, and analysis, but not all of them are suitable for the given scenario. For example, AWS Glue is a serverless data integration service that can help you discover, prepare, and combine data from various sources, but it requires you to create and run data extraction, transformation, and loading (ETL) jobs, which can add operational overhead3.
C . Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories. This option is not feasible, as AWS CodeCommit is a source control service that hosts secure Git-based repositories, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is a service that enables you to capture, process, and analyze data streams in real time, such as clickstream data, application logs, or IoT telemetry. It does not support accessing and integrating data from AWS CodeCommit repositories, which are meant for storing and managing code, not data .
D . Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR). This option is also not feasible, as Amazon ECR is a fully managed container registry service that stores, manages, and deploys container images, not a data source that can be accessed by Amazon Kinesis Data Streams. Amazon Kinesis Data Streams does not support accessing and integrating data from Amazon ECR, which is meant for storing and managing container images, not data .
Reference:
1: AWS Data Exchange User Guide
2: AWS Data Exchange FAQs
3: AWS Glue Developer Guide
: AWS CodeCommit User Guide
: Amazon Kinesis Data Streams Developer Guide
: Amazon Elastic Container Registry User Guide
: Build a Continuous Delivery Pipeline for Your Container Images with Amazon ECR as Source

NEW QUESTION # 132
A data engineer configured an AWS Glue Data Catalog for data that is stored in Amazon S3 buckets. The data engineer needs to configure the Data Catalog to receive incremental updates.
The data engineer sets up event notifications for the S3 bucket and creates an Amazon Simple Queue Service (Amazon SQS) queue to receive the S3 events.
Which combination of steps should the data engineer take to meet these requirements with LEAST operational overhead? (Select TWO.)

A. Manually initiate the AWS Glue crawler to perform updates to the Data Catalog when there is a change in the S3 bucket.
B. Create an S3 event-based AWS Glue crawler to consume events from the SQS queue.
C. Define a time-based schedule to run the AWS Glue crawler, and perform incremental updates to the Data Catalog.
D. Use AWS Step Functions to orchestrate the process of updating the Data Catalog based on 53 events that the SQS queue receives.
E. Use an AWS Lambda function to directly update the Data Catalog based on S3 events that the SQS queue receives.

Answer: B,E

Explanation:
The requirement is to update the AWS Glue Data Catalog incrementally based on S3 events. Using an S3 event-based approach is the most automated and operationally efficient solution.
* A. Create an S3 event-based AWS Glue crawler:
* An event-based Glue crawler can automatically update the Data Catalog when new data arrives in the S3 bucket. This ensures incremental updates with minimal operational overhead.
Reference:AWS Glue Event-Driven Crawlers
C: Use an AWS Lambda function to directly update the Data Catalog:
Lambda can be triggered by S3 events delivered to the SQS queue and can directly update the Glue Data Catalog, ensuring that new data is reflected in near real-time without running a full crawler.
Reference:Automating AWS Glue Data Catalog Updates
Alternatives Considered:
B (Time-based schedule): Scheduling a crawler to run periodically adds unnecessary latency and operational overhead.
D (Manual crawler initiation): Manually starting the crawler defeats the purpose of automation.
E (AWS Step Functions): Step Functions add complexity that is not needed when Lambda can handle the updates directly.
References:
AWS Glue Event-Driven Crawlers
Using AWS Lambda to Update Glue Catalog

NEW QUESTION # 133
......

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