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| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
| Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
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NEW QUESTION # 221
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?
Answer: B
Explanation:
The original query does not return results for all of the products because the year column in the sales_data table is not an integer, but a timestamp. Therefore, the WHERE clause does not filter the data correctly, and only returns the products that have a null value for the year column. To fix this, the data engineer should use the extract function to extract the year from the timestamp and compare it with 2023. This way, the querywill return the correct results for all of the products in the sales_data table. The other options are either incorrect or irrelevant, as they do not address the root cause of the issue. Replacing sum with count does not change the filtering condition, adding HAVING clause does not affect the grouping logic, and removing the GROUP BY clause does not solve the problem of missing products. References:
Troubleshooting JSON queries - Amazon Athena (Section: JSON related errors) When I query a table in Amazon Athena, the TIMESTAMP result is empty (Section: Resolution) AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 7, page 197)
NEW QUESTION # 222
A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.
The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.
Which solution will meet these requirements with the LOWEST latency?
Answer: A
Explanation:
This solution will meet the requirements with the lowest latency because it uses Amazon Managed Service for Apache Flink to process the sensor data in real time and write it to Amazon Timestream, a fast, scalable, and serverless time series database. Amazon Timestream is optimized for storing and analyzing time series data, such as sensor data, and can handle trillions of events per day with millisecond latency. By using Amazon Timestream as a source, you can create an Amazon QuickSight dashboard that displays a real-time view of operational efficiency on a large screen in the manufacturing facility. Amazon QuickSight is a fully managed business intelligence service that can connect to various data sources, including Amazon Timestream, and provide interactive visualizations and insights123.
The other options are not optimal for the following reasons:
* A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard. This option is similar to option C, but it uses Grafana instead of Amazon QuickSight to create the dashboard.
Grafana is an open source visualization tool that can also connect to Amazon Timestream, but it requires additional steps to set up and configure, such as deploying a Grafana server on Amazon EC2, installing the Amazon Timestream plugin, and creating an IAM role for Grafana to access Timestream.
These steps can increase the latency and complexity of the solution.
* B. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard. This option is not suitable for displaying a real-time view of operational efficiency, as it introduces unnecessary delays and costs in the data pipeline. First, the sensor data is written to an S3 bucket by Amazon Kinesis Data Firehose, which can have a buffering interval of up to 900 seconds. Then, the S3 bucket sends a notification to a Lambda function, which can incur additional invocation and execution time. Finally, the Lambda function publishes the data to Amazon Aurora, a relational database that is not optimized for time series data and can have higher storage and performance costs than Amazon Timestream .
* D. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
This option is also not suitable for displaying a real-time view of operational efficiency, as it uses AWS Glue bookmarks to read sensor data from the S3 bucket. AWS Glue bookmarks are a feature that helps AWS Glue jobs and crawlers keep track of the data that has already been processed, so that they can resume from where they left off. However, AWS Glue jobs and crawlers are not designed for real-time data processing, as they can have a minimum frequency of 5 minutes and a variable start-up time.
Moreover, this option also uses Grafana instead of Amazon QuickSight to create the dashboard, which can increase the latency and complexity of the solution .
References:
* 1: Amazon Managed Streaming for Apache Flink
* 2: Amazon Timestream
* 3: Amazon QuickSight
* : Analyze data in Amazon Timestream using Grafana
* : Amazon Kinesis Data Firehose
* : Amazon Aurora
* : AWS Glue Bookmarks
* : AWS Glue Job and Crawler Scheduling
NEW QUESTION # 223
A company saves customer data to an Amazon S3 bucket. The company uses server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the bucket. The dataset includes personally identifiable information (PII) such as social security numbers and account details.
Data that is tagged as PII must be masked before the company uses customer data for analysis. Some users must have secure access to the PII data during the preprocessing phase. The company needs a low-maintenance solution to mask and secure the PII data throughout the entire engineering pipeline.
Which combination of solutions will meet these requirements? (Select TWO.)
Answer: A,B
Explanation:
To address the requirement of masking PII data and ensuring secure access throughout the data pipeline, the combination of AWS Glue DataBrew and IAM provides a low-maintenance solution.
A . AWS Glue DataBrew for Masking:
AWS Glue DataBrew provides a visual tool to perform data transformations, including masking PII data. It allows for easy configuration of data transformation tasks without requiring manual coding, making it ideal for this use case.
Reference:
D . AWS Identity and Access Management (IAM):
Using IAM policies allows fine-grained control over access to PII data, ensuring that only authorized users can view or process sensitive data during the pipeline stages.
Alternatives Considered:
B (Amazon GuardDuty): GuardDuty is for threat detection and does not handle data masking or access control for PII.
C (Amazon Macie): Macie can help discover sensitive data but does not handle the masking of PII or access control.
E (Custom scripts): Custom scripting increases the operational burden compared to a built-in solution like DataBrew.
AWS Glue DataBrew for Data Masking
IAM Policies for PII Access Control
NEW QUESTION # 224
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.)
Answer: B,C
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:
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.
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.
AWS Glue Event-Driven Crawlers
Using AWS Lambda to Update Glue Catalog
NEW QUESTION # 225
A company has five offices in different AWS Regions. Each office has its own human resources (HR) department that uses a unique IAM role. The company stores employee records in a data lake that is based on Amazon S3 storage.
A data engineering team needs to limit access to the records. Each HR department should be able to access records for only employees who are within the HR department's Region.
Which combination of steps should the data engineering team take to meet this requirement with the LEAST operational overhead? (Choose two.)
Answer: A,E
Explanation:
AWS Lake Formation is a service that helps you build, secure, and manage data lakes on Amazon S3. You can use AWS Lake Formation to register the S3 path as a data lake location, and enable fine-grained access control to limit access to the records based on the HR department's Region. You can use data filters to specify which S3 prefixes or partitions each HR department can access, and grant permissions to the IAM roles of the HR departments accordingly. This solution will meet the requirement with the least operational overhead, as it simplifies the data lake management and security, and leverages the existing IAM roles of the HR departments12.
The other options are not optimal for the following reasons:
* A. Use data filters for each Region to register the S3 paths as data locations. This option is not possible, as data filters are not used to register S3 paths as data locations, but to grant permissions to access specific S3 prefixes or partitions within a data location. Moreover, this option does not specify how to limit access to the records based on the HR department's Region.
* C. Modify the IAM roles of the HR departments to add a data filter for each department's Region. This option is not possible, as data filters are not added to IAM roles, but to permissions granted by AWS Lake Formation. Moreover, this option does not specify how to register the S3 path as a data lake location, or how to enable fine-grained access control in AWS Lake Formation.
* E. Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region. This option is not recommended, as it would require more operational overhead to create and manage multiple S3 buckets, and to configure and maintain IAM policies for each HR department. Moreover, this option does not leverage the benefits of AWS Lake Formation, such as data cataloging, data transformation, and data governance.
1: AWS Lake Formation
2: AWS Lake Formation Permissions
3: AWS Identity and Access Management
4: Amazon S3
NEW QUESTION # 226
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