Data-Engineer-Associate Exam Dumps - Amazon Web Services AWS Certified Data Engineer Questions and Answers

A sort-merge join generates large shuffle files, leading to “No space left on device” errors when both datasets are large. Using a broadcast join sends a smaller dataset to all executors, avoiding shuffle and disk I/O overhead.

“Broadcast joins reduce shuffle I/O by distributing the smaller dataset to all worker nodes, mitigating disk space and shuffle errors.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This is the most cost-effective and direct fix for large shuffle-stage failures.

S3 Intelligent-Tiering is a storage class that automatically moves objects between four access tiers based on the changing access patterns. The default access tier consists of two tiers: Frequent Access and Infrequent Access. Objects in the Frequent Access tier have the same performance and availability as S3 Standard, while objects in the Infrequent Access tier have the same performance and availability as S3 Standard-IA. S3 Intelligent-Tiering monitors the access patterns of each object and moves them between the tiers accordingly, without any operational overhead or retrieval fees. This solution can optimize S3 storage costs for data with unpredictable and variable access patterns, while ensuring millisecond latency for data retrieval. The other solutions are not optimal or relevant for this requirement. Using S3 Storage Lens standard metrics and activity metrics can provide insights into the storage usage and access patterns, but they do not automate the data movement between storage classes. Creating S3 Lifecycle policies for the S3 buckets can move objects to more cost-optimized storage classes, but they require manual configuration and maintenance, and they may incur retrieval fees for data that is accessed unexpectedly. Activating the Deep Archive Access tier for S3 Intelligent-Tiering can further reduce the storage costs for data that is rarely accessed, but it also increases the retrieval time to 12 hours, which does not meet the requirement of millisecond latency. References:

S3 Intelligent-Tiering

S3 Storage Lens

S3 Lifecycle policies

[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]

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.”

Amazon DynamoDB is a serverless, low-latency, NoSQL database ideal for semi-structured data.

Adding DynamoDB Accelerator (DAX) provides microsecond response times for read-heavy workloads.

“For applications requiring millisecond or sub-millisecond reads with serverless operation, use DynamoDB with DAX caching.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

The most efficient and low-operational-overhead solution for ingesting data into Amazon Redshift from Amazon Kinesis Data Streams is to use Amazon Redshift streaming ingestion. This feature allows Redshift to directly ingest streaming data from Kinesis Data Streams and process it in real-time.

Amazon Redshift Streaming Ingestion:

Redshift supports native streaming ingestion from Kinesis Data Streams, allowing real-time data to be queried using materialized views.

This solution reduces operational complexity because you don't need intermediary services like Amazon Kinesis Data Firehose or S3 for batch loading.

The correct answer is D: Enable native Amazon RDS database audit logging with CloudWatch and CloudTrail integration.

Native RDS database audit logging captures SQL-level activities, including which user is modifying data, which is essential for meeting audit requirements.

Amazon CloudWatch Logs can be used to centralize and store logs.

CloudTrail logs API calls related to instance-level changes, ensuring all modifications are traceable.

This approach provides a comprehensive audit trail and supports compliance with security governance policies.

“Enable and configure native Amazon RDS database audit logging. Enable Amazon CloudWatch Logs. Configure metric filters and alarms. Configure AWS CloudTrail audit logging.”

To optimize joins between large tables, Redshift recommends using KEY distribution on a common, high-cardinality join key to ensure co-location of data blocks. The Amazon Redshift Advisor identifies performance bottlenecks and provides recommendations for distribution and sort keys.

“Use KEY distribution when joining large tables on a common high-cardinality column. Use Redshift Advisor to identify query optimization opportunities.”

Reserved concurrency allows you to dedicate a portion of your account’s available concurrency to a specific Lambda function. This ensures that the function always has sufficient capacity, regardless of how many other functions are running. It's more cost-effective than provisioned concurrency, which reserves warm environments at a higher cost.

“You can use reserved concurrency to guarantee that your critical Lambda functions have the capacity to run as needed without being throttled by other functions.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Provisioned concurrency would be overkill unless latency is also a concern for cold starts, which isn’t stated in this case.

Problem Analysis:

The company needs near real-time replication of MySQL updates to Amazon Redshift.

Minimal development effort is required for this solution.

Key Considerations:

AWS DMS provides a full load + CDC (Change Data Capture) mode for continuous replication of database changes.

DMS integrates natively with both MySQL and Redshift, simplifying setup.

Solution Analysis:

Option A: AWS Glue Job

Glue is batch-oriented and does not support near real-time replication.

Option B: DMS with Full Load + CDC

Efficiently handles initial database load and continuous updates.

Requires minimal setup and operational overhead.

Option C: AppFlow SDK

AppFlow is not designed for database replication. Custom connectors increase development effort.

Option D: DataSync

DataSync is for file synchronization and not suitable for database updates.

Final Recommendation:

Use AWS DMS in full load + CDC mode for continuous replication.

AWS Database Migration Service Documentation

Setting Up DMS with Redshift

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