Free Amazon Web Services Data-Engineer-Associate Practice Exam with Questions & Answers | Set: 6

For processing the incoming car listings in a cost-effective, scalable, and automated way, the ideal approach involves using AWS Glue for data processing, AWS Lambda with S3 Event Notifications for orchestration, Amazon Athena for one-time queries and analytical reporting, and Amazon QuickSight for visualization on the dashboard. Let’s break this down:

AWS Glue: This is a fully managed ETL (Extract, Transform, Load) service that automatically processes the incoming data files. Glue is serverless and supports diverse data sources, including Amazon S3 and Redshift.

AWS Lambda and S3 Event Notifications: Using Lambda and S3 Event Notifications allows near real-time triggering of processing workflows as soon as new data is uploaded into S3. This approach is event-driven, ensuring that the listings are processed as soon as they are uploaded, reducing the latency for data processing.

Amazon Athena: A serverless, pay-per-query service that allows interactive queries directly against data in S3 using standard SQL. It is ideal for the requirement of one-time queries and analytical reporting without the need for provisioning or managing servers.

Amazon QuickSight: A business intelligence tool that integrates with a wide range of AWS data sources, including Athena, and is used for creating interactive dashboards. It scales well and provides real-time insights for the car listings.

This solution (Option D) is the most cost-effective, because both Glue and Athena are serverless and priced based on usage, reducing costs when compared to provisioning EMR clusters in the other options. Moreover, using Lambda for orchestration is more cost-effective than AWS Step Functions due to its lightweight nature.

To allow cross-account access to a Kinesis Data Stream, you must add a resource-based policy to the Kinesis stream in Account A, explicitly granting the Lambda execution role in Account B the required permissions.

SCPs (A & C) set permissions boundaries, but do not grant access.

Option D incorrectly refers to the Lambda function – but the Kinesis resource must allow access.

“You must add a resource-based policy to the Kinesis Data Stream in Account A to allow a Lambda function in Account B to consume from the stream.”

AWS Glue Data Catalog stores metadata about data stored in Amazon S3, including table schemas and partitions. If new partitions are added to S3 and the Glue Data Catalog is not updated—either through crawlers or explicit API calls—query engines such as Amazon Athena and AWS Glue will not be aware of the new data.

This leads to analysis results that appear incomplete or outdated, even though the underlying data exists in S3. This is a common issue in time-sensitive analytics workloads where data arrives frequently and partitions change often.

IAM permission issues would typically cause access failures rather than missing records. S3 versioning affects object history, not query visibility. Overlapping Glue job schedules could cause processing delays but would not directly cause missing data during query time.

Therefore, the most likely cause is that the Glue Data Catalog metadata is not synchronized with the latest S3 partitions.

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.

When AWS Glue jobs run inside a private subnet, they must use secure and supported methods to access external databases. AWS Glue supports JDBC connections to on-premises databases, but best practices require secure credential management and explicit driver configuration.

Using a JDBC driver stored in Amazon S3 allows Glue to connect to PostgreSQL without relying on default drivers. Storing credentials in AWS Secrets Manager eliminates hard-coded credentials and enables secure rotation, aligning with AWS security best practices.

Simply specifying credentials inline is less secure and not recommended. SASL connections are not supported for PostgreSQL JDBC connections. Security groups alone do not establish connectivity or authentication.

Therefore, Option D is the correct and production-grade solution.

AWS Database Migration Service (AWS DMS) is a service that helps you migrate databases to AWS quickly and securely. You can use AWS DMS to migrate the Hive metastore from the on-premises Hadoop clusters into Amazon S3, which is a highly scalable, durable, and cost-effective object storage service. AWS Glue Data Catalog is a serverless, managed service that acts as a central metadata repository for your data assets. You can use AWS Glue Data Catalog to scan the Amazon S3 bucket that contains the migrated Hive metastore and create a data catalog that is compatible with Apache Hive and other AWS services. This solution meets the requirements of migrating the data catalog into a persistent storage solution and using a serverless solution. This solution is also the most cost-effective, as it does not incur any additional charges for running Amazon EMR or Amazon Aurora MySQL clusters. The other options are either not feasible or not optimal. Configuring a Hive metastore in Amazon EMR (option B) or an external Hive metastore in Amazon EMR (option C) would require running and maintaining Amazon EMR clusters, which would incur additional costs and complexity. Using Amazon Aurora MySQL to store the company’s data catalog (option C) would also incur additional costs and complexity, as well as introduce compatibility issues with Apache Hive. Configuring a new Hive metastore in Amazon EMR (option D) would not migrate the existing data catalog, but create a new one, which would result in data loss and inconsistency. References:

Using AWS Database Migration Service

Populating the AWS Glue Data Catalog

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Analysis and Visualization, Section 4.2: AWS Glue Data Catalog

The correct answer is D because Amazon Redshift row-level security provides granular access control over sensitive records by applying security policies at the database object level. This directly supports the requirement to let researchers access only records from their own departments without creating multiple copies of the dataset. Multi-condition SQL predicates are stronger than basic department-only filters because they can include department, role, sensitivity, and authorization logic in the same policy design. Creating separate schemas and views increases administrative overhead and can become inconsistent. Datashares are useful for sharing data but the option creates unnecessary departmental copies and does not solve record-level restriction by itself. Granting EXPLAIN RLS to researchers is also not needed for normal secure access.

Step 1: Understanding the Problem

The company collects clickstream data via Amazon Kinesis Data Streams and stores it in JSON format in Amazon S3 using Kinesis Data Firehose. They use Amazon Athena to query the data, but they want to reduce Athena costs while maintaining the same data pipeline.

Since Athena charges based on the amount of data scanned during queries, reducing the data size (by converting JSON to a more efficient format like Apache Parquet) is a key solution to lowering costs.

Step 2: Why Option A is Correct

Option A provides a straightforward way to reduce costs with minimal management overhead:

Changing the Firehose output format to Parquet: Parquet is a columnar data format, which is more compact and efficient than JSON for Athena queries. It significantly reduces the amount of data scanned, which in turn reduces Athena query costs.

Custom S3 Object Prefix (YYYYMMDD): Adding a date-based prefix helps in partitioning the data, which further improves query efficiency in Athena by limiting the data scanned to only relevant partitions.

AWS Glue ETL Job for Existing Data: To handle existing data stored in JSON format, a one-time AWS Glue ETL job can combine small JSON files, convert them to Parquet, and apply the YYYYMMDD prefix. This ensures consistency in the S3 bucket structure and allows Athena to efficiently query historical data.

ALTER TABLE ADD PARTITION: This command updates Athena ' s table metadata to reflect the new partitions, ensuring that future queries target only the required data.

Step 3: Why Other Options Are Not Ideal

Option B (Apache Spark on EMR) introduces higher management effort by requiring the setup of Apache Spark jobs and an Amazon EMR cluster. While it achieves the goal of converting JSON to Parquet, it involves running and maintaining an EMR cluster, which adds operational complexity.

Option C (Kinesis and Apache Flink) is a more complex solution involving Apache Flink, which adds a real-time streaming layer to aggregate data. Although Flink is a powerful tool for stream processing, it adds unnecessary overhead in this scenario since the company already uses Kinesis Data Firehose for batch delivery to S3.

Option D (AWS Lambda with Firehose) suggests using AWS Lambda to convert records in real time. While Lambda can work in some cases, it ' s generally not the best tool for handling large-scale data transformations like JSON-to-Parquet conversion due to potential scaling and invocation limitations. Additionally, running parallel Glue jobs further complicates the setup.

Step 4: How Option A Minimizes Costs

By using Apache Parquet, Athena queries become more efficient, as Athena will scan significantly less data, directly reducing query costs.

Firehose natively supports Parquet as an output format, so enabling this conversion in Firehose requires minimal effort. Once set, new data will automatically be stored in Parquet format in S3, without requiring any custom coding or ongoing management.

The AWS Glue ETL job for historical data ensures that existing JSON files are also converted to Parquet format, ensuring consistency across the data stored in S3.

Conclusion:

Option A meets the requirement to reduce Athena costs without recreating the data pipeline, using Firehose’s native support for Apache Parquet and a simple one-time AWS Glue ETL job for existing data. This approach involves minimal management effort compared to the other solutions.

The AWS Glue Data Catalog is an Apache Hive metastore-compatible catalog that provides a central metadata repository for various data sources and formats. You can use the AWS Glue Data Catalog as an external Hive metastore for Amazon EMR and Amazon Athena queries, and import metadata from existing Hive metastores into the Data Catalog. This solution requires the least development effort, as you can use AWS Glue crawlers to automatically discover and catalog the metadata from Hive, and use the AWS Glue console, AWS CLI, or Amazon EMR API to configure the Data Catalog as the Hive metastore. The other options are either more complex or require additional steps, such as setting up Apache Ranger for security, managing a Hive metastore on an EMR cluster or an RDS instance, or migrating the metadata manually. References:

Using the AWS Glue Data Catalog as the metastore for Hive (Section: Specifying AWS Glue Data Catalog as the metastore)

Metadata Management: Hive Metastore vs AWS Glue (Section: AWS Glue Data Catalog)

AWS Glue Data Catalog support for Spark SQL jobs (Section: Importing metadata from an existing Hive metastore)

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 131)