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

Amazon Athena achieves the fastest query performance when data is stored in columnar formats such as Apache Parquet and when queries can take advantage of partition pruning and predicate pushdown.

Converting CSV files to Parquet significantly reduces the amount of data scanned because Parquet stores data in a column-oriented layout. Since Athena queries only a subset of attributes, it reads only the required columns instead of scanning entire rows, which dramatically improves performance. Predicate pushdown further reduces query time by filtering data at the storage layer.

Partitioning the data by date ensures that Athena scans only the relevant partitions for same-day queries, minimizing unnecessary data reads. Storing one Parquet file per day is efficient and avoids the overhead of managing excessive small files.

ORC is also a columnar format, but Parquet is more commonly optimized and recommended for Athena workloads in AWS exam guidance. JSON and Avro are row-based or semi-row-based formats and result in larger scan sizes and slower query execution.

Therefore, Option D provides the shortest query times and aligns with Athena performance best practices.

 The error message indicates that the AWS Glue job cannot access the Amazon S3 bucket through the VPC endpoint. This could be because the VPC’s route table does not have the necessary routes to direct the traffic to the endpoint. To fix this, the data engineer must verify that the route table has an entry for the Amazon S3 service prefix (com.amazonaws.region.s3) with the target as the VPC endpoint ID. This will allow the AWS Glue job to use the VPC endpoint to access the S3 bucket without going through the internet or a NAT gateway. For more information, see Gateway endpoints. References:

Troubleshoot the AWS Glue error “VPC S3 endpoint validation failed”

Amazon VPC endpoints for Amazon S3

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

The company needs to use machine learning models for real-time inventory recommendations and future inventory predictions while leveraging both Amazon Redshift and Amazon SageMaker.

Option A: Use Amazon Redshift ML to generate inventory recommendations.Amazon Redshift ML allows you to build, train, and deploy machine learning models directly from Redshift using SQL statements. It integrates with SageMaker to train models and run inference. This feature is useful for generating inventory recommendations directly from the data stored in Redshift.

Option B: Use SQL to invoke a remote SageMaker endpoint for prediction.You can use SQL in Redshift to call a SageMaker endpoint for real-time inference. By invoking a SageMaker endpoint from within Redshift, the company can get real-time predictions on inventory, allowing for integration between the data warehouse and the machine learning model hosted in SageMaker.

Option C (offline model training) and Option D (creating dashboards with SageMaker Autopilot) are not relevant to the real-time prediction and recommendation requirements.

Option E (archiving inventory reports in Redshift) is not related to making predictions or recommendations.

This solution meets the requirements of managing the ingestion of real-time streaming data into AWS and performing real-time analytics on the incoming streaming data with the least operational overhead. Amazon Managed Service for Apache Flink is a fully managed service that allows you to run Apache Flink applications without having to manage any infrastructure or clusters. Apache Flink is a framework for stateful stream processing that supports various types of aggregations, such as tumbling, sliding, and session windows, over streaming data. By using Amazon Managed Service for Apache Flink, you can easily connect to Amazon Kinesis Data Streams as the source and sink of your streaming data, and perform time-based analytics over a window of up to 30 minutes. This solution is also highly fault tolerant, as Amazon Managed Service for Apache Flink automatically scales, monitors, and restarts your Flink applications in case of failures. References:

Amazon Managed Service for Apache Flink

Apache Flink

Window Aggregations in Flink

Option A is correct because Amazon Kinesis Data Streams is designed to collect and process large streams of data records in real time and is optimized for rapid, continuous data intake and aggregation. AWS states that Kinesis Data Streams is a fully managed, serverless data streaming service for ingesting streaming data at any scale and highlights event sources such as clickstream and application activity data. AWS also notes that Kinesis can pair with AWS Lambda for low-latency event processing. This directly matches the requirement to ingest millions of customer activity events with minimum latency in a scalable and durable way.

Option B is batch processing every 10 minutes, so it does not satisfy the real-time requirement. Option C depends on files landing in S3 first, which is also not the lowest-latency ingest path. Option D can support streaming, but a managed Kafka cluster with self-managed consumers introduces more operational overhead and is generally less cost-effective here than a native Kinesis + Lambda pipeline for an AWS-centric event-ingestion workload. Therefore, Kinesis Data Streams + Lambda + Redshift is the best answer.

Option B is correct because Amazon Q in QuickSight is the AWS feature built specifically for natural language interaction with business data. AWS documentation explains that Amazon Q in QuickSight lets users ask questions in natural language, generate summaries and data stories, and create visuals from author prompts. AWS Prescriptive Guidance also notes that users can generate insights with natural language prompts and that they do not have to write SQL queries or learn complex BI tooling. That matches the requirement that some analysts lack SQL knowledge and need to create reports frequently with minimal effort.

Option A is not the best answer because zero-ETL access addresses data movement and integration, not natural-language dashboard creation. Option C introduces another analytics platform and therefore more operational effort. Option D provides access to data, but federated querying still does not address the key requirement for natural-language dashboard and report generation for nontechnical analysts. The AWS-native feature that directly solves this problem with the least additional management is Amazon Q in QuickSight.

Option A is correct because when many AWS Glue jobs start at the same time in the same subnet, Glue needs to create network interfaces in that subnet. If the subnet does not have enough free private IP addresses available, jobs can fail with exactly this type of error. AWS service documentation for VPC-based managed data services consistently notes that if there is no available free IP address in a specified subnet, the service cannot create or add the required ENIs and the workload can fail or degrade.

This is a subnet-level exhaustion issue, not a VPC-wide issue. A VPC can still have free addresses in other subnets while the specific subnet chosen for the jobs is out of usable addresses. That makes D incorrect. Option B is incorrect because G.1X is a valid Glue worker type and does not inherently prevent subnet access. Option C is also incorrect because the error is about address availability, not IAM authorization. In practice, this problem is usually resolved by using a larger subnet or spreading workloads across additional subnets so enough IPs are available when multiple Glue jobs launch concurrently. This aligns with the exam’s focus on networking capacity planning for managed data services.

Problem Analysis:

The company processes 2 GB of daily sales records and 100 GB of Salesforce sales opportunities.

The goal is to analyze and correlate the two datasets with low operational overhead.

The process must run once nightly.

Key Considerations:

Amazon AppFlow simplifies data integration with Salesforce.

AWS Glue can extract data from MySQL and perform ETL operations.

Step Functions can orchestrate workflows with minimal manual intervention.

Apache Airflow and Flink add complexity, which conflicts with the requirement for low operational overhead.

Solution Analysis:

Option A: MWAA + Lambda + Step Functions

Requires custom Lambda code for dataset correlation, increasing development and operational complexity.

Option B: AppFlow + Glue + MWAA

MWAA adds orchestration overhead compared to the simpler Step Functions.

Option C: AppFlow + Glue + Step Functions

AppFlow fetches Salesforce data, Glue extracts MySQL data, and Step Functions orchestrate the entire process.

Minimal setup and operational overhead, making it the best choice.

Option D: AppFlow + Kinesis + Flink + Step Functions

Using Kinesis and Flink for batch processing introduces unnecessary complexity.

Final Recommendation:

Use Amazon AppFlow to fetch Salesforce data, AWS Glue to process MySQL data, and Step Functions for orchestration.

Amazon AppFlow Overview

AWS Glue ETL Documentation

AWS Step Functions

Option A is the most cost-effective because it enables seamless integration by allowing analysts to access and join external relational data directly from within Amazon Redshift for reporting, without building and operating a separate ingestion pipeline for a one-time (annual) workload. The study material frames Amazon Redshift as the centralized analytics store for complex queries and reporting, making it the natural place to perform the final transformations and analysis.

The alternatives introduce extra operational steps and recurring costs. Option B requires exporting large historical datasets to S3 and then loading them into Redshift, which increases data movement and operational complexity. Option C adds ETL job development, scheduling, retries, and monitoring overhead that is unnecessary if the primary goal is integrated querying for an annual report. Option D (DMS) is best when you need ongoing migration or continuous replication; it is typically more operationally heavy than needed for “query across sources” use cases and still requires additional setup and maintenance.

Because all databases are already in the same VPC and the analytics platform is Redshift, federated querying provides the most direct, lowest-operations path to integrate and analyze the data where it already needs to be consumed.