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** This is the ONLY course you need to ace the DEA-C01 exam in the first attempt **Welcome to the AWS Data Engineer Associate DEA-C01 - Practice Test Course!Are you preparing for the AWS DEA-C01 certification exam? This course is designed to help you succeed by providing high-quality practice tests that closely mirror the real exam.What You'll Get:190 Latest exam questions with detailed explanations to each answerRealistic Exam Simulation: My practice tests are designed to reflect the format, style, and difficulty of the official AWS Data Engineer Associate exam. This ensures you get a realistic testing experience.Comprehensive Coverage: The practice tests cover all the domains and objectives of the DEA-C01 exam:Domain 1: Data Ingestion and TransformationDomain 2: Data Store ManagementDomain 3: Data Operations and SupportDomain 4: Data Security and GovernanceDetailed Explanations: Each question comes with a detailed explanation to help you understand the concepts and reasoning behind the correct answers. This is crucial for deepening your knowledge and ensuring you're fully prepared. For each question, I have explained why an answer is correct and have also explained why other options are incorrect. You will also find supporting reference links for a quick read.Variety of Questions: You'll find a mix of multiple-choice, multiple-response, and scenario-based questions to fully prepare you for what to expect on exam day.Performance Tracking: Keep track of your progress with the test review feature. Identify your strengths and areas for improvement to focus your study efforts effectively.Sneak peak into what you will get inside the course:Q1. A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution that ingests sensor data in near real-time. The solution must store the data in a persistent data store. The solution must store the data in nested JSON format. The company must have the ability to query from the data store with a latency of less than 10 milliseconds. Which solution will meet these requirements with the LEAST operational overhead?A. Use a self-hosted Apache Kafka cluster to capture the sensor data. Store the data in Amazon S3 for querying.B. Use AWS Lambda to process the sensor data. Store the data in Amazon S3 for querying.C. Use Amazon Kinesis Data Streams to capture the sensor data. Store the data in Amazon DynamoDB for querying.D. Use Amazon Simple Queue Service (Amazon SQS) to buffer incoming sensor data. Use AWS Glue to store the data in Amazon RDS for querying.Answer/ExplanationOption C is CORRECT because Amazon Kinesis Data Streams can capture sensor data in near real-time, and Amazon DynamoDB can store the data in a persistent store with support for nested JSON format. DynamoDB provides low-latency querying, typically in single-digit milliseconds, which meets the requirement of querying with a latency of less than 10 milliseconds. This solution has the least operational overhead as it leverages fully managed AWS services that handle scaling and management tasks automatically.Option A is INCORRECT because a self-hosted Apache Kafka cluster requires significant operational overhead for management, scaling, and maintenance. Additionally, querying data from Amazon S3 would not meet the latency requirement of less than 10 milliseconds.Option B is INCORRECT because while AWS Lambda can process data in real-time, storing data in Amazon S3 does not provide the required low-latency querying capabilities. S3 is not optimized for low-latency query access.Option D is INCORRECT because using Amazon SQS to buffer data and AWS Glue to store data in Amazon RDS introduces additional latency and complexity. RDS, while capable of handling nested JSON, does not guarantee the sub-10 millisecond query latency required for this use case.Read More {Links available}Q2. A data engineer is building an automated extract, transform, and load (ETL) ingestion pipeline by using AWS Glue. The pipeline ingests compressed files that are in an Amazon S3 bucket. The ingestion pipeline must support incremental data processing. Which AWS Glue feature should the data engineer use to meet this requirement?A. WorkflowsB. TriggersC. Job bookmarksD. ClassifiersAnswer/ExplanationOption C is CORRECT because job bookmarks in AWS Glue enable incremental data processing. Job bookmarks track the state of the data that has been processed, allowing the ETL jobs to process only new or updated data since the last job run. This feature is essential for supporting incremental data processing in an automated ETL pipeline.Option A is INCORRECT because workflows in AWS Glue are used to create and manage complex ETL pipelines with multiple jobs and triggers. While workflows help orchestrate the ETL process, they do not specifically address the need for incremental data processing.Option B is INCORRECT because triggers in AWS Glue are used to start jobs based on a schedule or events. While triggers can help automate the ETL process, they do not provide the capability to track and process only the incremental data.Option D is INCORRECT because classifiers in AWS Glue are used to recognize the format and schema of data stored in Amazon S3. While classifiers help in understanding the data structure, they do not support incremental data processing.Read More {Links available}

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