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| Section | Weight | Objectives |
|---|---|---|
| Data Operations and Support | 22% | - Monitor and maintain data pipelines - Troubleshoot data workflow issues |
| Data Security and Governance | 18% | - Implement data security controls - Apply governance and compliance best practices |
| Data Store Management | 26% | - Select appropriate data storage solutions - Optimize storage performance and cost |
| Data Ingestion and Transformation | 34% | - Ingest and transform data using AWS services - Build and manage data pipelines |
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問題 #107
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?
答案：D
解題說明：
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.
問題 #108
A company has a data lake in Amazon 53. The company uses AWS Glue to catalog data and AWS Glue Studio to implement data extract, transform, and load (ETL) pipelines.
The company needs to ensure that data quality issues are checked every time the pipelines run. A data engineer must enhance the existing pipelines to evaluate data quality rules based on predefined thresholds.
Which solution will meet these requirements with the LEAST implementation effort?
答案：A
解題說明：
Problem Analysis:
The company uses AWS Glue for ETL pipelines and must enforce data quality checks during pipeline execution.
The goal is to implement quality checks with minimal implementation effort.
Key Considerations:
AWS Glue provides an Evaluate Data Quality transform that allows for defining quality checks directly in the pipeline.
DQDL (Data Quality Definition Language) simplifies the process by allowing declarative rule definitions.
Solution Analysis:
Option A: SQL Transform
SQL queries can implement rules but require manual effort for each rule and do not integrate natively with Glue.
Option B: Evaluate Data Quality Transform + DQDL
AWS Glue's built-in Evaluate Data Quality transform is designed for this use case.
Allows defining thresholds and rules in DQDL with minimal coding effort.
Option C: Custom Transform with PyDeequ
PyDeequ is a powerful library but adds unnecessary complexity compared to Glue's native features.
Option D: Custom Transform with Great Expectations
Similar to PyDeequ, Great Expectations adds operational complexity and external dependencies.
Final Recommendation:
Use the Evaluate Data Quality transform with DQDL to implement data quality rules in AWS Glue pipelines.
AWS Glue Data Quality
DQDL Syntax and Examples
AWS Glue Studio Documentation
問題 #109
A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column.
Which solution will MOST speed up the Athena query performance?
答案：D
解題說明：
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, changing the data format from CSV to Parquet and applying Snappy compression will most speed up the Athena query performance. 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. Snappy is a compression algorithm that reduces the data size without compromising the query speed, as it is splittable and does not require decompression before reading. 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 effective as changing the data format to Parquet and applying Snappy compression. Changing the data format from CSV to JSON and applying Snappy compression will not improve the query performance significantly, as JSON is also a row-oriented format that does not support columnar access or encoding techniques. Compressing the CSV files by using Snappy compression will reduce the data size, but it will not improve the query performance significantly, as CSV is still a row-oriented format that does not support columnar access or encoding techniques. Compressing the CSV files by using gzjg compression will reduce the data size, but it will degrade the query performance, as gzjg is not a splittable compression algorithm and requires decompression before reading. References:
* Amazon Athena
* Choosing the Right Data Format
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena
問題 #110
A company needs a solution to store and query product data that has variable attributes. The solution must support unpredictable and high-volume queries with single-digit millisecond latency, even during sudden traffic spikes. The solution must retrieve items by a primary identifier named Product ID. The solution must allow flexible queries by secondary attributes named Category and Brand.
Which solution will meet these requirements?
答案：C
解題說明：
Option A is the correct design for single-digit millisecond latency with unpredictable spikes and variable attributes. The study material describes Amazon DynamoDB as a NoSQL database "designed for highly dynamic datasets with frequent read and write operations," providing low-latency performance at any scale- which directly matches the latency and traffic-spike requirements.
DynamoDB's key-value and document model fits "product data that has variable attributes" because items can contain different attributes without needing schema migrations typical of relational databases. The requirement to retrieve items by Product ID maps naturally to DynamoDB's primary key access pattern. The requirement for flexible queries on Category and Brand is met by creating global secondary indexes (GSIs) on those attributes so queries can be served efficiently without scanning the whole table.
Option B (Aurora) can scale reads, but it is not typically the best fit for sustained single-digit millisecond performance during sudden spikes without careful capacity planning. Option C is optimized for search and text
/query relevance rather than primary-key transactional access patterns. Option D uses Athena (interactive SQL over S3) which is not designed for millisecond-latency, high-QPS query workloads.
問題 #111
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?
答案：C
解題說明：
* 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 toreduce processing timewhile using the existing AWS Glue framework.
* Key Considerations:
* AWS Glue offers thedynamic frame file-groupingfeature, 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:
* UseAWS Glue dynamic frame file-groupingfor optimized data ingestion and processing.
:
AWS Glue Dynamic Frames
Optimizing Glue Performance
問題 #112
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