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| Section | Weight | Objectives |
|---|---|---|
| Data Operations and Support | 22% | - Monitor and maintain data pipelines - Troubleshoot data workflow issues |
| Data Ingestion and Transformation | 34% | - Build and manage data pipelines - Ingest and transform data using AWS services |
| Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
| Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
>> Data-Engineer-Associate Certification Test Questions <<
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NEW QUESTION # 142
A company stores its processed data in an S3 bucket. The company has a strict data access policy. The company uses IAM roles to grant teams within the company different levels of access to the S3 bucket.
The company wants to receive notifications when a user violates the data access policy. Each notification must include the username of the user who violated the policy.
Which solution will meet these requirements?
Answer: C
Explanation:
The requirement is to detect violations of data access policies and receive notifications with the username of the violator. AWS CloudTrail can provideobject-level tracking for S3to capture detailed API actions on specific S3 objects, including the user who performed the action.
* AWS CloudTrail:
* CloudTrail can monitorAPI callsmade to an S3 bucket, including object-level API actions such as GetObject, PutObject, and DeleteObject. This will help detect access violations based on the API calls made by different users.
* CloudTrail logs include details such as theuser identity, which is essential for meeting the requirement of including the username in notifications.
* The CloudTrail logs can be forwarded toAmazon CloudWatchto trigger alarms based on certain access patterns (e.g., violations of specific policies).
Reference:Monitoring Amazon S3 Activity Using AWS CloudTrail
Amazon CloudWatch:
By forwarding CloudTrail logs to CloudWatch, you can set upalarmsthat are triggered when a specific condition is met, such as unauthorized access or policy violations. The alarm can include detailed information from the CloudTrail log, including the username.
Alternatives Considered:
A (AWS Config rules): While AWS Config can track resource configurations and compliance, it does not provide real-time, detailed tracking of object-level events like CloudTrail does.
B (CloudWatch metrics): CloudWatch does not gather object-level metrics for S3 directly. For this use case, CloudTrail provides better granularity.
D (S3 server access logs): S3 server access logs can monitor access, but they do not provide the real-time monitoring and alerting features that CloudTrail with CloudWatch alarms offer. They also do not include API- level granularity like CloudTrail.
References:
AWS CloudTrail Integration with S3
Amazon CloudWatch Alarms
NEW QUESTION # 143
A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce.
The company receives 2 GB erf sales records every day. The company has 100 GB of identified sales opportunities. A data engineer needs to develop a process that will analyze and correlate sales records and sales opportunities. The process must run once each night.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
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.
Reference:
Amazon AppFlow Overview
AWS Glue ETL Documentation
AWS Step Functions
NEW QUESTION # 144
A data engineer uses Amazon Redshift to run resource-intensive analytics processes once every month. Every month, the data engineer creates a new Redshift provisioned cluster. The data engineer deletes the Redshift provisioned cluster after the analytics processes are complete every month. Before the data engineer deletes the cluster each month, the data engineer unloads backup data from the cluster to an Amazon S3 bucket.
The data engineer needs a solution to run the monthly analytics processes that does not require the data engineer to manage the infrastructure manually.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
Amazon Redshift Serverless is a new feature of Amazon Redshift that enables you to run SQL queries on data in Amazon S3 without provisioning or managing any clusters. You can use Amazon Redshift Serverless to automatically process the analytics workload, as it scales up and down the compute resources based on the query demand, and charges you only for the resources consumed. This solution will meet the requirements with the least operational overhead, as it does not require the data engineer to create, delete, pause, or resume any Redshift clusters, or to manage any infrastructure manually. You can use the Amazon Redshift Data API to run queries from the AWS CLI, AWS SDK, or AWS Lambda functions12.
The other options are not optimal for the following reasons:
A: Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month. This option is not recommended, as it would still require the data engineer to create and delete a new Redshift provisioned cluster every month, which can incur additional costs and time. Moreover, this option would require the data engineer to use Amazon Step Functions to orchestrate the workflow of pausing and resuming the cluster, which can add complexity and overhead.
C: Use the AWS CLI to automatically process the analytics workload. This option is vague and does not specify how the AWS CLI is used to process the analytics workload. The AWS CLI can be used to run queries on data in Amazon S3 using Amazon Redshift Serverless, Amazon Athena, or Amazon EMR, but each of these services has different features and benefits. Moreover, this option does not address the requirement of not managing the infrastructure manually, as the data engineer may still need to provision and configure some resources, such as Amazon EMR clusters or Amazon Athena workgroups.
D: Use AWS CloudFormation templates to automatically process the analytics workload. This option is also vague and does not specify how AWS CloudFormation templates are used to process the analytics workload. AWS CloudFormation is a service that lets you model and provision AWS resources using templates. You can use AWS CloudFormation templates to create and delete a Redshift provisioned cluster every month, or to create and configure other AWS resources, such as Amazon EMR, Amazon Athena, or Amazon Redshift Serverless. However, this option does not address the requirement of not managing the infrastructure manually, as the data engineer may still need to write and maintain the AWS CloudFormation templates, and to monitor the status and performance of the resources.
References:
1: Amazon Redshift Serverless
2: Amazon Redshift Data API
3: Amazon Step Functions
4: AWS CLI
5: AWS CloudFormation
NEW QUESTION # 145
A company uses an Amazon QuickSight dashboard to monitor usage of one of the company's applications.
The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day.
A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs.
Which actions should the data engineer take to improve the performance of the AWS Glue jobs? (Choose two.)
Answer: B,E
Explanation:
Partitioning the data in the S3 bucket can improve the performance of AWS Glue jobs by reducing the amount of data that needs to be scanned and processed. By organizing the data by year, month, and day, the AWS Glue job can use partition pruning to filter out irrelevant data and only read the data that matches the query criteria. This can speed up the data processing and reduce the cost of running the AWS Glue job.
Increasing the AWS Glue instance size by scaling up the worker type can also improve the performance of AWS Glue jobs by providing more memory and CPU resources for the Spark execution engine. This can help the AWS Glue job handle larger data sets and complex transformations more efficiently. The other options are either incorrect or irrelevant, as they do not affect the performance of the AWS Glue jobs. Converting the AWS Glue schema to the DynamicFrame schema class does not improve the performance, but rather provides additional functionality and flexibility for data manipulation. Adjusting the AWS Glue job scheduling frequency does not improve the performance, but rather reduces the frequency of data updates. Modifying the IAM role that grants access to AWS Glue does not improve the performance, but rather affects the security and permissions of the AWS Glue service. References:
Optimising Glue Scripts for Efficient Data Processing: Part 1 (Section: Partitioning Data in S3) Best practices to optimize cost and performance for AWS Glue streaming ETL jobs (Section: Development tools) Monitoring with AWS Glue job run insights (Section: Requirements) AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 133)
NEW QUESTION # 146
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: D
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 # 147
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