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| Section | Weight | Objectives |
|---|---|---|
| Data Operations and Support | 22% | - Troubleshoot data workflow issues - Monitor and maintain data pipelines |
| Data Ingestion and Transformation | 34% | - Build and manage data pipelines - Ingest and transform data using AWS services |
| Data Security and Governance | 18% | - Apply governance and compliance best practices - Implement data security controls |
| Data Store Management | 26% | - Optimize storage performance and cost - Select appropriate data storage solutions |
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NEW QUESTION # 299
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,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 # 300
A healthcare company stores patient records in an on-premises MySQL database. The company creates an application to access the MySQL database. The company must enforce security protocols to protect the patient records. The company currently rotates database credentials every 30 days to minimize the risk of unauthorized access.
The company wants a solution that does not require the company to modify the application code for each credential rotation.
Which solution will meet this requirement with the least operational overhead?
Answer: B
Explanation:
The correct solution is C: AWS Secrets Manager.
* Why? AWS Secrets Manager is a fully managed service that helps you protect access to your applications, services, and IT resources without the upfront cost and complexity of managing your own hardware security module (HSM) infrastructure.
* It allows for automatic rotation of secrets without requiring changes to the application code, meeting the requirement of minimal operational overhead.
* Applications can securely retrieve credentials using Secrets Manager APIs, and the service integrates with AWS Identity and Access Management (IAM) to control access to secrets.
"You can use Secrets Manager to store credentials and to configure automatic rotation." Reference: AWS Certified Data Engineer - Associate Study Guide, Chapter 7 - Data Security and GovernanceAlso verified in AWS Documentation: https://docs.aws.amazon.com/secretsmanager/latest
/userguide/rotating-secrets.html
NEW QUESTION # 301
A company stores time-series data that is collected from streaming services in an Amazon S3 bucket. The company must ensure that only workloads that are deployed within the company's VPC can access the data.
Which solution will meet this requirement?
Answer: A
Explanation:
The best practice to restrict Amazon S3 access to specific VPCs is to use a bucket policy with a StringEquals or StringLike condition on aws:SourceVpc. This ensures only requests from a specified VPC are allowed.
* IAM policies (option C) control who can access the resource but are not suitable alone to restrict by VPC.
* Security Groups and NACLs (options B and D) do not apply to Amazon S3 because it is a global service and not VPC-bound.
"You can restrict access to your S3 bucket so that only requests coming from a specific VPC endpoint are allowed." Source: AWS Documentation - Amazon S3 Bucket Policies for VPC Endpoints
NEW QUESTION # 302
A company stores a large dataset in an Amazon S3 bucket. A data engineer frequently runs complex queries on the dataset by using Amazon Athena. The data engineer needs to optimize query performance and optimize costs for queries that are run multiple times with the same parameters.
Which solution will meet these requirements?
Answer: C
Explanation:
Comprehensive and Detailed Explanation (150-250 words)
Amazon Athena charges per amount of data scanned by each query. When the same query with identical parameters is executed multiple times, re-scanning the data repeatedly increases both cost and execution time.
Athena provides query result reuse, which allows previously computed query results to be reused when an identical query is rerun within a configurable time window.
By enabling query result reuse at the Athena workgroup level, Athena automatically returns cached results instead of re-reading data from Amazon S3. This significantly improves query performance while reducing data scan costs. This feature is specifically designed for workloads where analysts frequently rerun complex queries with the same parameters, which directly matches the scenario.
Converting data to JSON would degrade performance because JSON is not a columnar format and increases scan size. Using Amazon EMR introduces unnecessary infrastructure management and cost for a query optimization problem that Athena already solves natively. Amazon Redshift Spectrum is intended for querying S3 data from within Redshift and does not address repeated-query optimization in Athena.
Therefore, configuring query result reuse in the Athena workgroup is the most cost-effective and operationally efficient solution.
NEW QUESTION # 303
A retail company needs to implement a solution to capture data updates from multiple Amazon Aurora MySQL databases. The company needs to make the updates available for analytics in near real time. The solution must be serverless and require minimal maintenance.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: C
Explanation:
Option D is correct because Aurora zero-ETL integration with Amazon Redshift is the AWS-managed feature built specifically to make transactional Aurora data available in Amazon Redshift Serverless in near real time.
AWS documents that Aurora zero-ETL is a fully managed solution that makes transactional data available in the analytics destination after it is written to the Aurora cluster, eliminating the need to build and maintain complex ETL pipelines. AWS also states that Redshift zero-ETL supports a target data warehouse that can be a Redshift Serverless workgroup, which directly matches the question.
Option A and C add more operational components, such as DMS tasks, replication design, schema handling, and additional streaming infrastructure. Option B is also more operationally heavy because MSK Connect with Debezium requires Kafka-based CDC infrastructure and connector management. Since the requirement emphasizes serverless, near real-time analytics, and least operational overhead, the native zero-ETL integration is the best fit. This also aligns with the study guide's focus on choosing AWS services that minimize management effort while supporting ingestion and analytics workflows.
NEW QUESTION # 304
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