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| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion and Transformation | 34% | - Perform data ingestion
|
| Data Operations and Support | 22% | - Manage and troubleshoot data processes
|
| Data Security and Governance | 18% | - Implement data quality checks
|
| Data Store Management | 26% | - Understand data cataloging
|
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NEW QUESTION # 102
A company ingests data from multiple data sources and stores the data in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake.
The company needs to identify matching records even when the records do not have a common unique identifier.
Which solution will meet this requirement?
Answer: B
Explanation:
The problem described requires identifying matching records even when there is no unique identifier. AWS Lake FormationFindMatchesis designed for this purpose. It uses machine learning (ML) to deduplicate and find matching records in datasets that do not share a common identifier.
* D. Train and use the AWS Lake Formation FindMatches transform in the ETL job:
* FindMatchesis a transform available in AWS Lake Formation that uses ML to discover duplicate records or related records that might not have a common unique identifier.
* It can be integrated into an AWS Glue ETL job to perform deduplication or matching tasks.
* FindMatches is highly effective in scenarios where records do not share a key, such as customer records from different sources that need to be merged or reconciled.
Reference:AWS Lake Formation FindMatches
Alternatives Considered:
A (Amazon Made pattern matching): Amazon Made is not a service in AWS, and pattern matching typically refers to regular expressions, which are not suitable for deduplication without a common identifier.
B (AWS Glue PySpark Filter class): PySpark's Filter class can help refine datasets, but it does not offer the ML-based matching capabilities required to find matches between records without unique identifiers.
C (Partition tables on a unique identifier): Partitioning requires a unique identifier, which the question states is unavailable.
References:
AWS Glue Documentation on Lake Formation FindMatches
FindMatches in AWS Lake Formation
NEW QUESTION # 103
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?
Answer: B
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 # 104
A company stores logs in an Amazon S3 bucket. When a data engineer attempts to access several log files, the data engineer discovers that some files have been unintentionally deleted.
The data engineer needs a solution that will prevent unintentional file deletion in the future.
Which solution will meet this requirement with the LEAST operational overhead?
Answer: C
Explanation:
To prevent unintentional file deletions and meet the requirement with minimal operational overhead, enabling S3 Versioningis the best solution.
* S3 Versioning:
* S3 Versioning allows multiple versions of an object to be stored in the same S3 bucket. When a file is deleted or overwritten, S3 preserves the previous versions, which means you canrecover from accidental deletions or modifications.
* Enabling versioning requires minimal overhead, as it is abucket-level settingand does not require additional backup processes or data replication.
* Users can recover specific versions of files that were unintentionally deleted, meeting the needs of the data engineer to avoid accidental data loss.
Reference:Amazon S3 Versioning
Alternatives Considered:
A (Manual backups): Manually backing up the bucket requires higher operational effort and maintenance compared to enabling S3 Versioning, which is automated.
C (S3 Replication): Replication ensures data is copied to another bucket but does not provide protection against accidental deletion. It would increase operational costs without solving the core issue of accidental deletion.
D (S3 Glacier): Storing data in Glacier provides long-term archival storage but is not designed to prevent accidental deletion. Glacier is also more suitable for archival and infrequently accessed data, not for active logs.
References:
Amazon S3 Versioning Documentation
S3 Data Protection Best Practices
NEW QUESTION # 105
A company stores data in a data lake that is in Amazon S3. Some data that the company stores in the data lake contains personally identifiable information (PII). Multiple user groups need to access the raw dat a. The company must ensure that user groups can access only the PII that they require.
Which solution will meet these requirements with the LEAST effort?
Answer: D
Explanation:
Amazon Athena is a serverless, interactive query service that enables you to analyze data in Amazon S3 using standard SQL. AWS Lake Formation is a service that helps you build, secure, and manage data lakes on AWS. You can use AWS Lake Formation to create data filters that define the level of access for different IAM roles based on the columns, rows, or tags of the data. By using Amazon Athena to query the data and AWS Lake Formation to create data filters, the company can meet the requirements of ensuring that user groups can access only the PII that they require with the least effort. The solution is to use Amazon Athena to query the data in the data lake that is in Amazon S3. Then, set up AWS Lake Formation and create data filters to establish levels of access for the company's IAM roles. For example, a data filter can allow a user group to access only the columns that contain the PII that they need, such as name and email address, and deny access to the columns that contain the PII that they do not need, such as phone number and social security number. Finally, assign each user to the IAM role that matches the user's PII access requirements. This way, the user groups can access the data in the data lake securely and efficiently. The other options are either not feasible or not optimal. Using Amazon QuickSight to access the data (option B) would require the company to pay for the QuickSight service and to configure the column-level security features for each user. Building a custom query builder UI that will run Athena queries in the background to access the data (option C) would require the company to develop and maintain the UI and to integrate it with Amazon Cognito. Creating IAM roles that have different levels of granular access (option D) would require the company to manage multiple IAM roles and policies and to ensure that they are aligned with the data schema. Reference:
Amazon Athena
AWS Lake Formation
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Analysis and Visualization, Section 4.3: Amazon Athena
NEW QUESTION # 106
A company needs to implement a new inventory management system that provides near real-time updates and visibility across all AWS Regions. The new solution must provide centralized access control over data access and permissions. The company has a separate inventory management team assigned to each Region. Each inventory management team needs to update inventory levels.
A data engineer must implement Amazon Redshift data sharing with write capabilities. The solution must follow the principle of least privilege.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
Amazon Redshift data sharing now supports bi-directional and selective write access through datashare namespaces.
This allows centralized management of permissions from headquarters while giving Regional clusters controlled write access to specific datasets - adhering to least privilege and minimizing admin overhead.
"Use Redshift datashares with write permissions and namespaces to enable controlled cross-Region data collaboration while maintaining centralized governance."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This configuration ensures near real-time inventory updates, centralized access management, and compliance with AWS security best practices.
NEW QUESTION # 107
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