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Amazon Data-Engineer-Associate Exam Syllabus Topics:

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

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q102-Q107):

NEW QUESTION # 102
A company currently uses a provisioned Amazon EMR cluster that includes general purpose Amazon EC2 instances. The EMR cluster uses EMR managed scaling between one to five task nodes for the company's long-running Apache Spark extract, transform, and load (ETL) job. The company runs the ETL job every day.
When the company runs the ETL job, the EMR cluster quickly scales up to five nodes. The EMR cluster often reaches maximum CPU usage, but the memory usage remains under 30%.
The company wants to modify the EMR cluster configuration to reduce the EMR costs to run the daily ETL job.
Which solution will meet these requirements MOST cost-effectively?

Answer: A

Explanation:
The company's Apache Spark ETL job on Amazon EMR uses high CPU but low memory, meaning that compute-optimized EC2 instances would be the most cost-effective choice. These instances are designed for high-performance compute applications, where CPU usage is high, but memory needs are minimal, which is exactly the case here.
Compute Optimized Instances:
Compute-optimized instances, such as the C5 series, provide a higher ratio of CPU to memory, which is more suitable for jobs with high CPU usage and relatively low memory consumption.
Switching from general-purpose EC2 instances to compute-optimized instances can reduce costs while improving performance, as these instances are optimized for workloads like Spark jobs that perform a lot of computation.
Reference:
Managed Scaling: The EMR cluster's scaling is currently managed between 1 and 5 nodes, so changing the instance type will leverage the current scaling strategy but optimize it for the workload.
Alternatives Considered:
A (Increase task nodes to 10): Increasing the number of task nodes would increase costs without necessarily improving performance. Since memory usage is low, the bottleneck is more likely the CPU, which compute-optimized instances can handle better.
B (Memory optimized instances): Memory-optimized instances are not suitable since the current job is CPU-bound, and memory usage remains low (under 30%).
D (Reduce scaling cooldown): This could marginally improve scaling speed but does not address the need for cost optimization and improved CPU performance.
Amazon EMR Cluster Optimization
Compute Optimized EC2 Instances


NEW QUESTION # 103
A telecommunications company collects network usage data throughout each day at a rate of several thousand data points each second. The company runs an application to process the usage data in real time. The company aggregates and stores the data in an Amazon Aurora DB instance.
Sudden drops in network usage usually indicate a network outage. The company must be able to identify sudden drops in network usage so the company can take immediate remedial actions.
Which solution will meet this requirement with the LEAST latency?

Answer: B

Explanation:
The telecommunications company needs a low-latency solution to detect sudden drops in network usage from real-time data collected throughout the day.
* Option B: Modify the processing application to publish the data to an Amazon Kinesis data stream. Create an Amazon Managed Service for Apache Flink (Amazon Kinesis Data Analytics) application to detect drops in network usage.Using Amazon Kinesis with Managed Service for Apache Flink (formerly Kinesis Data Analytics) is ideal for real-time stream processing with minimal latency. Flink can analyze the incoming data stream in real-time and detect anomalies, such as sudden drops in usage, which makes it the best fit for this scenario.
Other options (A, C, and D) either introduce unnecessary delays (e.g., querying databases) or do not provide the same real-time, low-latency processing that is critical for this use case.
References:
* Amazon Kinesis Data Analytics for Apache Flink
* Amazon Kinesis Documentation


NEW QUESTION # 104
A company uses Amazon S3 buckets, AWS Glue tables, and Amazon Athena as components of a data lake.
Recently, the company expanded its sales range to multiple new states. The company wants to introduce state names as a new partition to the existing S3 bucket, which is currently partitioned by date.
The company needs to ensure that additional partitions will not disrupt daily synchronization between the AWS Glue Data Catalog and the S3 buckets.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: A

Explanation:
Scheduling an AWS Glue crawler to periodically update the Data Catalog automates the process of detecting new partitions and updating the catalog, which minimizes manual maintenance and operational overhead.


NEW QUESTION # 105
A company uses AWS Glue jobs to implement several data pipelines. The pipelines are critical to the company.
The company needs to implement a monitoring mechanism that will alert stakeholders if the pipelines fail.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
Creating an EventBridge rule that triggers a Lambda function on AWS Glue job failure events and then sends notifications via Amazon SNS is the most direct and operationally efficient method:
"Practice Quiz 10: A data engineer must monitor the data pipeline... Which solution will meet these requirements?
A). Inspect the job run monitoring section of the AWS Glue console.answer: A."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Although this reference directly supports using AWS Glue's monitoring features via EventBridge, it implies that solutions like A-which directly use EventBridge failure events for automation-are more optimal and less complex than constructing custom logs and metrics.


NEW QUESTION # 106
A sales company uses AWS Glue ETL to collect, process, and ingest data into an Amazon S3 bucket. The AWS Glue pipeline creates a new file in the S3 bucket every hour. File sizes vary from 200 KB to 300 KB.
The company wants to build a sales prediction model by using data from the previous 5 years. The historic data includes 44,000 files.
The company builds a second AWS Glue ETL pipeline by using the smallest worker type. The second pipeline retrieves the historic files from the S3 bucket and processes the files for downstream analysis. The company notices significant performance issues with the second ETL pipeline.
The company needs to improve the performance of the second pipeline.
Which solution will meet this requirement MOST cost-effectively?

Answer: B

Explanation:
When processingmany small files, the overhead of managing partitions can degrade performance. Rather than scaling workers manually, enablingAWS Glue auto scalingdynamically adjusts worker count based on resource requirements, which iscost-effectiveand addresses the performance concern.
"AWS Glue auto scaling automatically adds or removes workers based on the workload, which is efficient for handling performance bottlenecks when working with many small files."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Manually scaling up workers or increasing their size (A, B) can be more expensive and less adaptive.


NEW QUESTION # 107
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