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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Operations and Support | 22% | - Automate data pipelines
|
| Topic 2: Data Store Management | 26% | - Choose a data store
|
| Topic 3: Data Security and Governance | 18% | - Apply authentication and authorization
|
| Topic 4: Data Ingestion and Transformation | 34% | - Transform and process data
|
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NEW QUESTION # 189
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: A,B
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 ofcolumns. 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. References:
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 # 190
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?
Answer: C
Explanation:
Explanation: 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.
NEW QUESTION # 191
A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary.
The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to useData Quality Definition Language (DQDL)rules in an AWS Glue Data Quality job.
Which rule will meet these requirements?
Answer: B
Explanation:
TheDatasetMatchrule in DQDL checks for full value equivalence between mapped fields. A value of1.0 indicates a100% match. The correct syntax and metric for an exact match scenario are:
"Use DatasetMatch when comparing mapped fields between two datasets. The comparison score of 1.0 confirms a perfect match."
-Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Options with "100" use incorrect syntax since DQDL usesfloating-point scores(e.g., 1.0, 0.95), not percentages.
NEW QUESTION # 192
A company has a gaming application that stores data in Amazon DynamoDB tables. A data engineer needs to ingest the game data into an Amazon OpenSearch Service cluster. Data updates must occur in near real time.
Which solution will meet these requirements?
Answer: C
Explanation:
Problem Analysis:
The company uses DynamoDB for gaming data storage and needs to ingest data into Amazon OpenSearch Service in near real time.
Data updates must propagate quickly to OpenSearch for analytics or search purposes.
Key Considerations:
DynamoDB Streams provide near-real-time capture of table changes (inserts, updates, and deletes).
Integration with AWS Lambda allows seamless processing of these changes.
OpenSearch offers APIs for indexing and updating documents, which Lambda can invoke.
Solution Analysis:
Option A: Step Functions with Periodic Export
Not suitable for near-real-time updates; introduces significant latency.
Operationally complex to manage periodic exports and S3 data ingestion.
Option B: AWS Glue Job
AWS Glue is designed for ETL workloads but lacks real-time processing capabilities.
Option C: DynamoDB Streams + Lambda
DynamoDB Streams capture changes in near real time.
Lambda can process these streams and use the OpenSearch API to update the index.
This approach provides low latency and seamless integration with minimal operational overhead.
Option D: Custom OpenSearch Plugin
Writing a custom plugin adds complexity and is unnecessary with existing AWS integrations.
Implementation Steps:
Enable DynamoDB Streams for the relevant DynamoDB tables.
Create a Lambda function to process stream records:
Parse insert, update, and delete events.
Use OpenSearch APIs to index or update documents based on the event type.
Set up a trigger to invoke the Lambda function whenever there are changes in the DynamoDB Stream.
Monitor and log errors for debugging and operational health.
Reference:
Amazon DynamoDB Streams Documentation
AWS Lambda and DynamoDB Integration
Amazon OpenSearch Service APIs
NEW QUESTION # 193
A company wants to combine data from multiple software as a service (SaaS) applications for analysis.
A data engineering team needs to use Amazon QuickSight to perform the analysis and build dashboards. A data engineer needs to extract the data from the SaaS applications and make the data available for QuickSight queries.
Which solution will meet these requirements in the MOST operationally efficient way?
Answer: A
NEW QUESTION # 194
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