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| Section | Weight | Objectives |
|---|---|---|
| Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
| 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 Ingestion and Transformation | 34% | - Build and manage data pipelines - Ingest and transform data using AWS services |
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NEW QUESTION # 197
A company stores sales data in an Amazon RDS for MySQL database. The company needs to start a reporting process between 6:00 A.M. and 6:10 A.M. every Monday. The reporting process must generate a CSV file and store the file in an Amazon S3 bucket.
Which combination of steps will meet these requirements with the LEAST operational overhead? (Select TWO.)
Answer: A,D
Explanation:
The Amazon EventBridge Scheduler offers a simple, serverless cron-based execution mechanism. It can trigger an AWS Glue ETL job that extracts data from Amazon RDS, formats it as CSV, and writes it to Amazon S3 - all without manual orchestration or servers.
"For scheduled data extraction and transformation, use AWS Glue jobs triggered by EventBridge Scheduler for fully managed, low-maintenance workflows."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Glue natively integrates with RDS and S3, avoiding the need to manage Batch or EMR infrastructure.
NEW QUESTION # 198
A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file.
Which Step Functions state should the data engineer use to meet these requirements?
Answer: D
Explanation:
Option C is the correct answer because the Map state is designed to process a collection of data in parallel by applying the same transformation to each element. The Map state can invoke a nested workflow for each element, which can be another state machine or a Lambda function. The Map state will wait until all the parallel executions are completed before moving to the next state.
Option A is incorrect because the Parallel state is used to execute multiple branches of logic concurrently, not to process a collection of data. The Parallel state can have different branches with different logic and states, whereas the Map state has only one branch that is applied to each element of the collection.
Option B is incorrect because the Choice state is used to make decisions based on a comparison of a value to a set of rules. The Choice state does not process any data or invoke any nested workflows.
Option D is incorrect because the Wait state is used to delay the state machine from continuing for a specified time. The Wait state does not process any data or invoke any nested workflows.
:
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Orchestration, Section 5.3: AWS Step Functions, Pages 131-132 Building Batch Data Analytics Solutions on AWS, Module 5: Data Orchestration, Lesson 5.2: AWS Step Functions, Pages 9-10 AWS Documentation Overview, AWS Step Functions Developer Guide, Step Functions Concepts, State Types, Map State, Pages 1-3
NEW QUESTION # 199
Files from multiple data sources arrive in an Amazon S3 bucket on a regular basis. A data engineer wants to ingest new files into Amazon Redshift in near real time when the new files arrive in the S3 bucket.
Which solution will meet these requirements?
Answer: C
Explanation:
For near real-time processing of new files in S3,event-driven ingestionis optimal. S3 Event Notifications can triggerAWS Lambdato immediately load data into Amazon Redshift, eliminating latency associated with batch scheduling.
"Event-based triggers using S3 notifications and Lambda functions are effective for near real-time ingestion pipelines into Amazon Redshift."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Option C (Glue bookmarks) is best for batch jobs, and zero-ETL applies toAurora to Redshift, not S3.
NEW QUESTION # 200
A company is building an analytics solution. The solution uses Amazon S3 for data lake storage and Amazon Redshift for a data warehouse. The company wants to use Amazon Redshift Spectrum to query the data that is in Amazon S3.
Which actions will provide the FASTEST queries? (Choose two.)
Answer: C,D
Explanation:
Amazon Redshift Spectrum is a feature that allows you to run SQL queries directly against data in Amazon S3, without loading or transforming the data. Redshift Spectrum can query 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, using a columnar storage file format, such as Parquet, will provide faster queries, as it allows Redshift Spectrum to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. Additionally, partitioning the data based on the most common query predicates, such as date, time, region, etc., will provide faster queries, as it allows Redshift Spectrum to prune the partitions that do not match the query criteria, reducing the amount of data scanned from S3. Partitioning also improves the performance of joins and aggregations, as it reduces data skew and shuffling.
The other options are not as effective as using a columnar storage file format and partitioning the data. Using gzip compression to compress individual files to sizes that are between 1 GB and 5 GB will reduce the data size, but it will not improve the query performance significantly, as gzip is not a splittable compression algorithm and requires decompression before reading. Splitting the data into files that are less than 10 KB will increase the number of files and the metadata overhead, which will degrade the query performance. Using file formats that are not supported by Redshift Spectrum, such as XML, will not work, as Redshift Spectrum will not be able to read or parse the data. Reference:
Amazon Redshift Spectrum
Choosing the Right Data Format
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Lakes and Data Warehouses, Section 4.3: Amazon Redshift Spectrum
NEW QUESTION # 201
A company needs a solution to manage costs for an existing Amazon DynamoDB table. The company also needs to control the size of the table. The solution must not disrupt any ongoing read or write operations. The company wants to use a solution that automatically deletes data from the table after 1 month.
Which solution will meet these requirements with the LEAST ongoing maintenance?
Answer: C
Explanation:
The requirement is to manage the size of an Amazon DynamoDB table by automatically deleting data older than 1 month without disrupting ongoing read or write operations. The simplest and most maintenance-free solution is to use DynamoDB Time-to-Live (TTL).
Option A: Use the DynamoDB TTL feature to automatically expire data based on timestamps.DynamoDB TTL allows you to specify an attribute (e.g., a timestamp) that defines when items in the table should expire.
After the expiration time, DynamoDB automatically deletes the items, freeing up storage space and keeping the table size under control without manual intervention or disruptions to ongoing operations.
Other options involve higher maintenance and manual scheduling or scanning operations, which increase complexity unnecessarily compared to the native TTL feature.
References:
DynamoDB Time-to-Live (TTL)
NEW QUESTION # 202
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