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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
| Topic 2: Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Topic 3: Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
| Topic 4: Data Security and Governance | 18% | - Implement data security controls - Apply governance and compliance best practices |
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NEW QUESTION # 33
A data engineer is launching an Amazon EMR duster. The data that the data engineer needs to load into the new cluster is currently in an Amazon S3 bucket. The data engineer needs to ensure that data is encrypted both at rest and in transit.
The data that is in the S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The data engineer has an Amazon S3 path that has a Privacy Enhanced Mail (PEM) file.
Which solution will meet these requirements?
Answer: B
Explanation:
The data engineer needs to ensure that the data in an Amazon EMR cluster is encrypted both at rest and in transit. The data in Amazon S3 is already encrypted using an AWS KMS key. To meet the requirements, the most suitable solution is to create an EMR security configuration that specifies the correct KMS key for at-rest encryption and use the PEM file for in-transit encryption.
Option C: Create an Amazon EMR security configuration. Specify the appropriate AWS KMS key for at-rest encryption for the S3 bucket. Specify the Amazon S3 path of the PEM file for in-transit encryption. Use the security configuration during EMR cluster creation.
This option configures encryption for both data at rest (using KMS keys) and data in transit (using the PEM file for SSL/TLS encryption). This approach ensures that data is fully protected during storage and transfer.
Options A, B, and D either involve creating unnecessary additional security configurations or make inaccurate assumptions about the way encryption configurations are attached.
Reference:
Amazon EMR Security Configuration
Amazon S3 Encryption
NEW QUESTION # 34
A company is developing a product recommendation system that uses Amazon OpenSearch Service. The system needs to perform k-nearest neighbors (k-NN) vector searches on 10 million product embeddings with
768-dimensional vectors. The system must maintain high recall accuracy and support incremental updates without reindexing as new products are added each day. The system must also accommodate complex filtering based on product categories and inventory status.
Which vector index type will meet these requirements?
Answer: C
Explanation:
The correct answer is B because the scenario requires scalable approximate vector search, high recall, incremental updates, and complex filtering. Amazon OpenSearch Service supports k-NN vector search for recommendation use cases, and OpenSearch supports k-NN vector fields for high-dimensional vector search. The Lucene HNSW option is strongest here because OpenSearch documentation specifically states that Lucene supports k-NN searches using HNSW graphs and supports Lucene filters for k-NN searches. That directly matches the need for category and inventory filtering. Exact k-NN with Painless script scoring is accurate but too slow for 10 million vectors. IVF can be efficient but is less ideal for frequent incremental updates and complex filtering. Binary quantization reduces memory but sacrifices accuracy.
NEW QUESTION # 35
A data engineer wants to optimize the runtime performance of an AWS Glue extract, transform, and load (ETL) job. The job processes large JSON files from Amazon S3. The job currently reads all fields from the source files but transforms only a subset of the fields.
Which solution will meet these requirements?
Answer: A
Explanation:
The correct answer is B because the performance issue is caused by reading unnecessary data. The best optimization is to reduce the amount of data and schema fields that the AWS Glue job reads before transformation. Pushdown-style filtering and selecting only required fields reduce scan and serialization overhead, especially when the job transforms only a subset of the JSON structure. Job bookmarks help track previously processed data, but they do not reduce the amount of data read within each file. Splitting the job into several smaller jobs increases orchestration and does not solve unnecessary field reads. An AWS Glue crawler catalogs metadata; it does not convert JSON files into Parquet. Therefore, restricting the source schema and pushing filtering closer to the source is the best available answer.
NEW QUESTION # 36
A data engineer needs to build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 MB.
Which solution will meet these requirements MOST cost-effectively?
Answer: C
Explanation:
AWS Glue is a fully managed serverless ETL service that can handle various data sources and formats, including .csv files in Amazon S3. AWS Glue provides two types of jobs: PySpark and Python shell. PySpark jobs use Apache Spark to process large-scale data in parallel, while Python shell jobs use Python scripts to process small-scale data in a single execution environment. For this requirement, a Python shell job is more suitable and cost-effective, as the size of each S3 object is less than 100 MB, which does not require distributed processing. A Python shell job can use pandas, a popular Python library for data analysis, to transform the .csv data as needed. The other solutions are not optimal or relevant for this requirement. Writing a custom Python application and hosting it on an Amazon EKS cluster would require more effort and resources to set up and manage the Kubernetes environment, as well as to handle the data ingestion and transformation logic. Writing a PySpark ETL script and hosting it on an Amazon EMR cluster would also incur more costs and complexity to provision and configure the EMR cluster, as well as to use Apache Spark for processing small data files. Writing an AWS Glue PySpark job would also be less efficient and economical than a Python shell job, as it would involve unnecessary overhead and charges for using Apache Spark for small data files. Reference:
AWS Glue
Working with Python Shell Jobs
pandas
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]
NEW QUESTION # 37
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 # 38
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