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| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion and Transformation | 34% | - Transform and enrich data
- Ingest data from various sources
|
| Data Store Management | 26% | - Optimize storage performance and cost - Manage data lifecycle and storage tiers - Design and implement data storage solutions
|
| Data Operations and Support | 22% | - Monitor and troubleshoot data pipelines
- Backup, restore, and disaster recovery - Automate operational tasks |
| Data Security and Governance | 18% | - Encrypt data at rest and in transit - Enforce compliance and data governance
- Implement access control and authentication
|
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NEW QUESTION # 301
A media company wants to build a real-time analytics pipeline to process customer activity events across the company ' s website and mobile app. The company wants to build a solution to ingest millions of events with minimum latency. The solution must be scalable and durable enough so that no data is lost.
Which solution will meet these requirements in the MOST cost-effective way?
Answer: B
Explanation:
Option A is correct because Amazon Kinesis Data Streams is designed to collect and process large streams of data records in real time and is optimized for rapid, continuous data intake and aggregation. AWS states that Kinesis Data Streams is a fully managed, serverless data streaming service for ingesting streaming data at any scale and highlights event sources such as clickstream and application activity data. AWS also notes that Kinesis can pair with AWS Lambda for low-latency event processing. This directly matches the requirement to ingest millions of customer activity events with minimum latency in a scalable and durable way.
Option B is batch processing every 10 minutes, so it does not satisfy the real-time requirement. Option C depends on files landing in S3 first, which is also not the lowest-latency ingest path. Option D can support streaming, but a managed Kafka cluster with self-managed consumers introduces more operational overhead and is generally less cost-effective here than a native Kinesis + Lambda pipeline for an AWS-centric event- ingestion workload. Therefore, Kinesis Data Streams + Lambda + Redshift is the best answer.
NEW QUESTION # 302
A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.
A data engineer needs to find a solution to process the streaming dat
a. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: D
Explanation:
The streaming ingestion feature of Amazon Redshift enables you to ingest data from streaming sources, such as Amazon Kinesis Data Streams, into Amazon Redshift tables in near real-time. You can use the streaming ingestion feature to process the streaming data from the wearable devices, hospital equipment, and patient records. The streaming ingestion feature also supports incremental updates, which means you can append new data or update existing data in the Amazon Redshift tables. This way, you can store the data in an Amazon Redshift Serverless warehouse and support near real-time analytics of the streaming data and the previous day's data. This solution meets the requirements with the least operational overhead, as it does not require any additional services or components to ingest and process the streaming data. The other options are either not feasible or not optimal. Loading data into Amazon Kinesis Data Firehose and then into Amazon Redshift (option A) would introduce additional latency and cost, as well as require additional configuration and management. Loading data into Amazon S3 and then using the COPY command to load the data into Amazon Redshift (option C) would also introduce additional latency and cost, as well as require additional storage space and ETL logic. Using the Amazon Aurora zero-ETL integration with Amazon Redshift (option D) would not work, as it requires the data to be stored in Amazon Aurora first, which is not the case for the streaming data from the healthcare company. Reference:
Using streaming ingestion with Amazon Redshift
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.5: Amazon Redshift Streaming Ingestion
NEW QUESTION # 303
A company stores raw clickstream data in an Amazon S3 bucket. The company needs a solution to process the data every day by using complex PySpark transformations that rely on custom internal libraries. After the data is transformed, the company must store the data in Amazon Redshift for analytics. The solution must be highly scalable to handle large data workloads.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: A
Explanation:
Option A is correct because AWS Glue is a serverless ETL service built to run PySpark workloads at scale with minimal infrastructure management. AWS documentation states that you can install additional Python modules and libraries for use with AWS Glue ETL jobs, including by using the --additional-python-modules parameter and Amazon S3 paths for wheel artifacts or other supported package delivery methods. That directly addresses the requirement for custom internal libraries. Since the data is already in Amazon S3 and the result must be loaded into Amazon Redshift, Glue is a natural low-overhead fit for this daily transformation pipeline.
Option B and C require managing compute infrastructure or cluster lifecycle, which increases operational overhead. Option D is not the best fit because SageMaker Processing is designed primarily for ML-oriented data preparation, not as the standard AWS service for large-scale ETL into Redshift. The question explicitly asks for least operational overhead with scalable PySpark and custom libraries, and AWS Glue provides exactly that managed capability.
NEW QUESTION # 304
A company receives test results from testing facilities that are located around the world. The company stores the test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process the files, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The data engineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and Amazon EventBridge to schedule jobs.
The company recently added more testing facilities. The time required to process files is increasing. The data engineer must reduce the data processing time.
Which solution will MOST reduce the data processing time?
Answer: D
Explanation:
Problem Analysis:
Millions of 1 KB JSON files in S3 are being processed and converted to Apache Parquet format using AWS Glue.
Processing time is increasing due to the additional testing facilities.
The goal is to reduce processing time while using the existing AWS Glue framework.
Key Considerations:
AWS Glue offers the dynamic frame file-grouping feature, which consolidates small files into larger, more efficient datasets during processing.
Grouping smaller files reduces overhead and speeds up processing.
Solution Analysis:
Option A: Lambda for File Grouping
Using Lambda to group files would add complexity and operational overhead. Glue already offers built-in grouping functionality.
Option B: AWS Glue Dynamic Frame File-Grouping
This option directly addresses the issue by grouping small files during Glue job execution.
Minimizes data processing time with no extra overhead.
Option C: Redshift COPY Command
COPY directly loads raw files but is not designed for pre-processing (conversion to Parquet).
Option D: Amazon EMR
While EMR is powerful, replacing Glue with EMR increases operational complexity.
Final Recommendation:
Use AWS Glue dynamic frame file-grouping for optimized data ingestion and processing.
Reference:
AWS Glue Dynamic Frames
Optimizing Glue Performance
NEW QUESTION # 305
A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The .csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the dataset. The data analysts rarely query the entire file.
Which solution will meet these requirements MOST cost-effectively?
Answer: B
Explanation:
Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. Athena supports various data formats, such as CSV, JSON, ORC, Avro, and Parquet. However, not all data formats are equally efficient for querying. Some data formats, such as CSV and JSON, are row-oriented, meaning that they store data as a sequence of records, each with the same fields. Row-oriented formats are suitable for loading and exporting data, but they are not optimal for analytical queries that often access only a subset of columns. Row-oriented formats also do not support compression or encoding techniques that can reduce the data size and improve the query performance.
On the other hand, some data formats, such as ORC and Parquet, are column-oriented, meaning that they store data as a collection of columns, each with a specific data type. Column-oriented formats are ideal for analytical queries that often filter, aggregate, or join data by columns. Column-oriented formats also support compression and encoding techniques that can reduce the data size and improve the query performance. For example, Parquet supports dictionary encoding, which replaces repeated values with numeric codes, and run-length encoding, which replaces consecutive identical values with a single value and a count. Parquet also supports various compression algorithms, such as Snappy, GZIP, and ZSTD, that can further reduce the data size and improve the query performance.
Therefore, creating an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source and writing the data into the data lake in Apache Parquet format will meet the requirements most cost-effectively. AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue ETL jobs allow you to transform and load data from various sources into various targets, using either a graphical interface (AWS Glue Studio) or a code-based interface (AWS Glue console or AWS Glue API). By using AWS Glue ETL jobs, you can easily convert the data from CSV to Parquet format, without having to write or manage any code. Parquet is a column-oriented format that allows Athena to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. This solution will also reduce the cost of Athena queries, as Athena charges based on the amount of data scanned from S3.
The other options are not as cost-effective as creating an AWS Glue ETL job to write the data into the data lake in Parquet format. Using an AWS Glue PySpark job to ingest the source data into the data lake in .csv format will not improve the query performance or reduce the query cost, as .csv is a row-oriented format that does not support columnar access or compression. Creating an AWS Glue ETL job to ingest the data into the data lake in JSON format will not improve the query performance or reduce the query cost, as JSON is also a row-oriented format that does not support columnar access or compression. Using an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format will improve the query performance, as Avro is a column-oriented format that supports compression and encoding, but it will require more operational effort, as you will need to write and maintain PySpark code to convert the data from CSV to Avro format. Reference:
Amazon Athena
Choosing the Right Data Format
AWS Glue
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena
NEW QUESTION # 306
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