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Amazon Data-Engineer-Associate Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Operations and Support22%- Automate data pipelines
  • 1. Scheduling jobs
  • 2. Event-driven triggers
  • 3. AWS Lambda triggers
- Monitor data pipelines
  • 1. Amazon CloudWatch
  • 2. AWS CloudTrail
  • 3. Logging and metrics
- Manage and troubleshoot data processes
  • 1. Debugging failed jobs
  • 2. Cost optimization
  • 3. Performance tuning
Topic 2: Data Ingestion and Transformation34%- Perform data ingestion
  • 1. Throughput and latency characteristics for AWS services
  • 2. Streaming data ingestion
  • 3. Replayability of data
  • 4. Batch data ingestion (scheduled ingestion, event-driven ingestion)
  • 5. Data ingestion patterns (frequency and data history)
- Transform and process data
  • 1. Data transformation services (AWS Glue, Amazon EMR, AWS Lambda)
  • 2. Data partitioning and compression
  • 3. Batch and stream processing
  • 4. ETL/ELT patterns
- Orchestrate data pipelines
  • 1. Amazon Managed Workflows for Apache Airflow (MWAA)
  • 2. AWS Glue Workflows
  • 3. AWS Step Functions
  • 4. Event-driven architectures
- Apply programming concepts
  • 1. Version control
  • 2. SQL, Python, Scala
  • 3. Infrastructure as Code (IaC)
Topic 3: Data Store Management26%- Choose a data store
  • 1. Data characteristics (structured, semi-structured, unstructured)
  • 2. Access and storage patterns
  • 3. Data lakes vs. data warehouses
  • 4. Amazon S3, Amazon RDS, Amazon DynamoDB, Amazon Redshift
- Manage data lifecycle
  • 1. Data archiving
  • 2. Data retention policies
  • 3. Amazon S3 storage classes
- Design data models
  • 1. Normalization and denormalization
  • 2. Partitioning and indexing strategies
  • 3. Schema design
- Understand data cataloging
  • 1. Data discovery and classification
  • 2. AWS Glue Data Catalog
  • 3. Schema evolution
Topic 4: Data Security and Governance18%- Ensure data encryption
  • 1. Encryption at rest and in transit
  • 2. AWS KMS
- Apply authentication and authorization
  • 1. Service control policies (SCPs)
  • 2. AWS IAM policies and roles
  • 3. Amazon S3 bucket policies
- Implement data quality checks
  • 1. Data validation
  • 2. AWS Glue DataBrew
- Manage data privacy and compliance
  • 1. PII data handling
  • 2. AWS Lake Formation permissions
  • 3. Data masking and tokenization

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q277-Q282):

NEW QUESTION # 277
A technology company currently uses Amazon Kinesis Data Streams to collect log data in real time. The company wants to use Amazon Redshift for downstream real-time queries and to enrich the log data.
Which solution will ingest data into Amazon Redshift with the LEAST operational overhead?

Answer: A

Explanation:
The most efficient and low-operational-overhead solution for ingesting data into Amazon Redshift from Amazon Kinesis Data Streams is to useAmazon 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 usingmaterialized views.
* This solution reduces operational complexity because you don't need intermediary services like Amazon Kinesis Data Firehose or S3 for batch loading.
Reference:Amazon Redshift Streaming Ingestion
Alternatives Considered:
A (Data Firehose to Redshift): This option is more suitable for batch processing but incurs additional operational overhead with the Firehose setup.
B (Firehose to S3): This involves an intermediate step, which adds complexity and delays the real-time requirement.
C (Managed Service for Apache Flink): This would work but introduces unnecessary complexity compared to Redshift's native streaming ingestion.
References:
Amazon Redshift Streaming Ingestion from Kinesis
Materialized Views in Redshift


NEW QUESTION # 278
A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data.
The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
AWS Glue workflows are a feature of AWS Glue that enable you to create and visualize complex ETL pipelines using AWS Glue components, such as crawlers, jobs, triggers, and development endpoints. AWS Glue workflows provide automated orchestration and require minimal manual effort, as they handle dependency resolution, error handling, state management, and resource allocation for your ETL workflows.
You can use AWS Glue workflows to ingest data from your operational databases into your Amazon S3 based data lake, and then use AWS Glue and Amazon EMR to process the data in the data lake. This solution will meet the requirements with the least operational overhead, as it leverages the serverless and fully managed nature of AWS Glue, and the scalability and flexibility of Amazon EMR12.
The other options are not optimal for the following reasons:
* B. AWS Step Functions tasks. AWS Step Functions is a service that lets you coordinate multiple AWS services into serverless workflows. You can use AWS Step Functions tasks to invoke AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use AWS Step Functions state machines to define the logic and flow of your workflows. However, this option would require more manual effort than AWS Glue workflows, as you would need to write JSON code to define your state machines, handle errors and retries, and monitor the execution history and status of your workflows3.
* C. AWS Lambda functions. AWS Lambda is a service that lets you run code without provisioning or managing servers. You can use AWS Lambda functions to trigger AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use AWS Lambda event sources and destinations to orchestrate the flow of your workflows. However, this option would also require more manual effort than AWS Glue workflows, as you would need to write code to implement your business logic, handle errors and retries, and monitor the invocation and execution of your Lambda functions. Moreover, AWS Lambda functions have limitations on the execution time, memory, and concurrency, which may affect the performance and scalability of your ETL workflows.
* D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows. Amazon MWAA is a managed service that makes it easy to run open source Apache Airflow on AWS. Apache Airflow is a popular tool for creating and managing complex ETL pipelines using directed acyclic graphs (DAGs).
You can use Amazon MWAA workflows to orchestrate AWS Glue and Amazon EMR jobs as part of your ETL workflows, and use the Airflow web interface to visualize and monitor your workflows.
However, this option would have more operational overhead than AWS Glue workflows, as you would need to set up and configure your Amazon MWAA environment, write Python code to define your DAGs, and manage the dependencies and versions of your Airflow plugins and operators.
1: AWS Glue Workflows
2: AWS Glue and Amazon EMR
3: AWS Step Functions
4: AWS Lambda
5: Amazon Managed Workflows for Apache Airflow


NEW QUESTION # 279
A data engineer is creating a product recommendation system that requires vector search across 5 million product embeddings with 768 dimensions. The system must prioritize search accuracy and maintain search latency under 100 milliseconds. The data engineer wants to implement a k-nearest neighbors (k-NN) vector index in Amazon OpenSearch Service.
Which vector index type should the data engineer use?

Answer: C

Explanation:
The correct answer is B because HNSW with the Faiss engine is the best fit when the system needs approximate k-NN search over millions of high-dimensional vectors while maintaining low latency and strong recall. OpenSearch approximate k-NN is designed for large, high-dimensional datasets where brute-force exact search becomes inefficient, and OpenSearch documentation shows HNSW configured with the Faiss engine for knn_vector fields. Exact search with a scoring script is accurate but does not scale well for 5 million vectors under a strict latency requirement. IVF can scale but usually requires training and tuning and may trade off recall. Binary quantization reduces memory but can reduce accuracy. Therefore, HNSW with Faiss is the strongest exam answer.


NEW QUESTION # 280
A company plans to use Amazon Kinesis Data Firehose to store data in Amazon S3. The source data consists of 2 MB csv files. The company must convert the .csv files to JSON format. The company must store the files in Apache Parquet format.
Which solution will meet these requirements with the LEAST development effort?

Answer: A

Explanation:
The company wants to use Amazon Kinesis Data Firehose to transform CSV files into JSON format and store the files in Apache Parquet format with the least development effort.
* Option B: Use Kinesis Data Firehose to convert the CSV files to JSON and to store the files in Parquet format.Kinesis Data Firehose supports data format conversion natively, including converting incoming CSV data to JSON format and storing the resulting files in Parquet format in Amazon S3.
This solution requires the least development effort because it uses built-in transformation features of Kinesis Data Firehose.
Other options (A, C, D) involve invoking AWS Lambda functions, which would introduce additional complexity and development effort compared to Kinesis Data Firehose's native format conversion capabilities.
References:
* Amazon Kinesis Data Firehose Documentation


NEW QUESTION # 281
A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: A

Explanation:
Option B is the best solution to meet the requirements with the least operational overhead because S3 Select is a feature that allows you to retrieve only a subset of data from an S3 object by using simple SQL expressions.
S3 Select works on objects stored in CSV, JSON, or Parquet format. By using S3 Select, you can avoid the need to download and process the entire S3 object, which reduces the amount of data transferred and the computation time. S3 Select is also easy to use and does not require any additional services or resources.
Option A is not a good solution because it involves writing custom code and configuring an AWS Lambda function to load data from the S3 bucket into a pandas dataframe and query the required column. This option adds complexity and latency to the data retrieval process and requires additional resources and configuration.Moreover, AWS Lambda has limitations on the execution time, memory, and concurrency, which may affect the performance and reliability of the data retrieval process.
Option C is not a good solution because it involves creating and running an AWS Glue DataBrew project to consume the S3 objects and query the required column. AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. However, in this scenario, the data is already in Parquet format, which is a columnar storage format that is optimized for analytics.
Therefore, there is no need to use AWS Glue DataBrew to prepare the data. Moreover, AWS Glue DataBrew adds extra time and cost to the data retrieval process and requires additional resources and configuration.
Option D is not a good solution because it involves running an AWS Glue crawler on the S3 objects and using a SQL SELECT statement in Amazon Athena to query the required column. An AWS Glue crawler is a service that can scan data sources and create metadata tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores information about the data sources, such as schema, format, and location.
Amazon Athena is a serverless interactive query service that allows you to analyze data in S3 using standard SQL. However, in this scenario, the schema and format of the data are already known and fixed, so there is no need to run a crawler to discover them. Moreover, running a crawler and using Amazon Athena adds extra time and cost to the data retrieval process and requires additional services and configuration.
References:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
S3 Select and Glacier Select - Amazon Simple Storage Service
AWS Lambda - FAQs
What Is AWS Glue DataBrew? - AWS Glue DataBrew
Populating the AWS Glue Data Catalog - AWS Glue
What is Amazon Athena? - Amazon Athena


NEW QUESTION # 282
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