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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q273-Q278):

NEW QUESTION # 273
A company uses Amazon S3 to store semi-structured data in a transactional data lake. Some of the data files are small, but other data files are tens of terabytes.
A data engineer must perform a change data capture (CDC) operation to identify changed data from the data source. The data source sends a full snapshot as a JSON file every day and ingests the changed data into the data lake.
Which solution will capture the changed data MOST cost-effectively?

Answer: A

Explanation:
An open source data lake format, such as Apache Parquet, Apache ORC, or Delta Lake, is a cost-effective way to perform a change data capture (CDC) operation on semi-structured data stored in Amazon S3. An open source data lake format allows you to query data directly from S3 using standard SQL, without the need to move or copy data to another service. An open source data lake format also supports schema evolution, meaning it can handle changes in the data structure over time. An open source data lake format also supports upserts, meaning it can insert new data and update existing data in the same operation, using a merge command. This way, you can efficiently capture the changes from the data source and apply them to the S3 data lake, without duplicating or losing any data.
The other options are not as cost-effective as using an open source data lake format, as they involve additional steps or costs. Option A requires you to create and maintain an AWS Lambda function, which can be complex and error-prone. AWS Lambda also has some limits on the execution time, memory, and concurrency, which can affect the performance and reliability of the CDC operation. Option B and D require you to ingest the data into a relational database service, such as Amazon RDS or Amazon Aurora, which can be expensive and unnecessary for semi-structured data. AWS Database Migration Service (AWS DMS) can write the changed data to the data lake, but it also charges you for the data replication and transfer. Additionally, AWS DMS does not support JSON as a source data type, so you would need to convert the data to a supported format before using AWS DMS. Reference:
What is a data lake?
Choosing a data format for your data lake
Using the MERGE INTO command in Delta Lake
[AWS Lambda quotas]
[AWS Database Migration Service quotas]


NEW QUESTION # 274
A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

Answer: B

Explanation:
Concurrency scaling is a feature that allows you to support thousands of concurrent users and queries, with consistently fast query performance. When you turn on concurrency scaling, Amazon Redshift automatically adds query processing power in seconds to process queries without any delays. You can manage which queries are sent to the concurrency-scaling cluster by configuring WLM queues. To turn on concurrency scaling for a queue, set the Concurrency Scaling mode value to auto. The other options are either incorrect or irrelevant, as they do not enable concurrency scaling for the existing Redshift cluster on RA3 nodes.
References:
* Working with concurrency scaling - Amazon Redshift
* Amazon Redshift Concurrency Scaling - Amazon Web Services
* Configuring concurrency scaling queues - Amazon Redshift
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 6, page 163)


NEW QUESTION # 275
A company needs to build a data lake in AWS. The company must provide row-level data access and column-level data access to specific teams. The teams will access the data by using Amazon Athena, Amazon Redshift Spectrum, and Apache Hive from Amazon EMR.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
Option D is the best solution to meet the requirements with the least operational overhead because AWS Lake Formation is a fully managed service that simplifies the process of building, securing, and managing data lakes. AWS Lake Formation allows you to define granular data access policies at the row and column level for different users and groups. AWS Lake Formation also integrates with Amazon Athena, Amazon Redshift Spectrum, and Apache Hive on Amazon EMR, enabling these services to access the data in the data lake through AWS Lake Formation.
Option A is not a good solution because S3 access policies cannot restrict data access by rows and columns.
S3 access policies are based on the identity and permissions of the requester, the bucket and object ownership, and the object prefix and tags. S3 access policies cannot enforce fine-grained data access control at the row and column level.
Option B is not a good solution because it involves using Apache Ranger and Apache Pig, which are not fully managed services and require additional configuration and maintenance. Apache Ranger is a framework that provides centralized security administration for data stored in Hadoop clusters, such as Amazon EMR. Apache Ranger can enforce row-level and column-level access policies for Apache Hive tables. However, Apache Ranger is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters. Apache Pig is a platform that allows you to analyze large data sets using a high-level scripting language called Pig Latin. Apache Pig can access data stored in Amazon S3 and process it using Apache Hive.
However,Apache Pig is not a native AWS service and requires manual installation and configuration on Amazon EMR clusters.
Option C is not a good solution because Amazon Redshift is not a suitable service for data lake storage.
Amazon Redshift is a fully managed data warehouse service that allows you to run complex analytical queries using standard SQL. Amazon Redshift can enforce row-level and column-level access policies for different users and groups. However, Amazon Redshift is not designed to store and process large volumes of unstructured or semi-structured data, which are typical characteristics of data lakes. Amazon Redshift is also more expensive and less scalable than Amazon S3 for data lake storage.
References:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
What Is AWS Lake Formation? - AWS Lake Formation
Using AWS Lake Formation with Amazon Athena - AWS Lake Formation
Using AWS Lake Formation with Amazon Redshift Spectrum - AWS Lake Formation Using AWS Lake Formation with Apache Hive on Amazon EMR - AWS Lake Formation Using Bucket Policies and User Policies - Amazon Simple Storage Service Apache Ranger Apache Pig What Is Amazon Redshift? - Amazon Redshift


NEW QUESTION # 276
A data engineer needs to schedule a workflow that runs a set of AWS Glue jobs every day. The data engineer does not require the Glue jobs to run or finish at a specific time.
Which solution will run the Glue jobs in the MOST cost-effective way?

Answer: C

Explanation:
The FLEX execution class allows you to run AWS Glue jobs on spare compute capacity instead of dedicated hardware. This can reduce the cost of running non-urgent or non-time sensitive data integration workloads, such as testing and one-time data loads. The FLEX execution class is available for AWS Glue 3.0 Spark jobs. The other options are not as cost-effective as FLEX, because they either use dedicated resources (STANDARD) or do not affect the cost at all (Spot Instance type and GlueVersion). Reference:
Introducing AWS Glue Flex jobs: Cost savings on ETL workloads
Serverless Data Integration - AWS Glue Pricing
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 125)


NEW QUESTION # 277
A data engineer is configuring an AWS Glue Apache Spark extract, transform, and load (ETL) Job. The job contains a sort-merge join of two large and equally sized DataFrames.
The job is failing with the following error: No space left on device.
Which solution will resolve the error?

Answer: A


NEW QUESTION # 278
......

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