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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q84-Q89):
NEW QUESTION # 84
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?
- A. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) Connect with Debezium connectors to load data into Amazon Redshift Serverless.
- B. Set up AWS Database Migration Service (AWS DMS) tasks that perform schema conversions for each database. Load the changes into Amazon Redshift Serverless.
- C. Use AWS Database Migration Service (AWS DMS) to set up binary log replication to Amazon Kinesis Data Streams. Load the data into Amazon Redshift Serverless after schema conversion.
- D. Use Aurora zero-ETL integrations with Amazon Redshift Serverless for each database to load Aurora MySQL changes in Amazon Redshift Serverless.
Answer: D
Explanation:
Aurora zero-ETL integration with Amazon Redshift Serverless is the most direct fit because it delivers near real-time data availability for analytics with a fully managed approach and minimal maintenance. It avoids building and operating custom CDC pipelines, streaming infrastructure, or connector frameworks, which gives it the least operational overhead for multiple Aurora MySQL databases.
NEW QUESTION # 85
A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.
The company stores all order data at a single path within the S3 bucket. The data has more than
100 columns. The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.
The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded. A data engineer must resolve the performance issues for the queries.
Which combination of steps will meet this requirement with LEAST developmental effort?
(Choose two.)
- A. Partition the order data in the S3 bucket based on order date.
- B. Configure the third-party application to create the files in a columnar format.
- C. Develop an AWS Glue ETL job to convert the multiple daily CSV files to one file for each day.
- D. Configure the third-party application to create the files in JSON format.
- E. Load the JSON data into the Amazon Redshift table in a SUPER type column.
Answer: A,B
Explanation:
A columnar format like Parquet or ORC optimizes query performance, especially when using services like Redshift Spectrum. Redshift Spectrum allows querying data directly in S3, and columnar formats help reduce the amount of data scanned during queries because only the needed columns are read. This reduces I/O and speeds up the query performance without needing to load all the columns of the dataset. This change is highly beneficial, especially when querying large datasets with many columns like in this scenario.
Partitioning the data in Amazon S3 helps Redshift Spectrum prune unnecessary data, improving query performance. Partitioning by a frequently filtered column like order date allows Redshift Spectrum to scan only relevant partitions, reducing the amount of data that needs to be processed. This leads to faster query times.
While combining files might reduce the number of files and improve performance slightly, it doesn't address the core issue of optimizing the data format and partitioning, which have a much bigger impact on performance.
JSON is not an efficient format for large-scale analytics and tends to have worse performance compared to columnar formats. Columnar formats like Parquet or ORC are preferable in this case.
The SUPER type is useful for semi-structured data in Redshift but isn't directly related to improving query performance in this scenario, where columnar formats and partitioning would provide more benefit.
NEW QUESTION # 86
A company runs concurrent analytical queries on Amazon Redshift tables multiple times each day. The queries require consistent data views three times each day. The company runs extract, transform, and load (ETL) operations that update dimension tables while the queries run.
The company has noticed that the queries cause table-level locks during the ETL operations. The company's current solution experiences query timeouts and deadlocks during peak processing hours, which affects analytical reporting and on-demand analysis.
Which solution will fix this issue?
- A. Use Amazon Redshift materialized views for analytical queries. Schedule ETL operations during off-peak hours to minimize lock contention.
- B. Deploy separate Amazon Redshift clusters for ETL and analytics workloads. Use cross-database queries and data sharing to maintain data consistency.
- C. Use Amazon Redshift Spectrum to query data in Amazon S3 for analytical workloads. Maintain ETL operations on Amazon Redshift tables with transaction isolation.
- D. Configure Amazon Redshift federated queries to access source data directly. Use read replicas to isolate analytical workloads from ETL operations.
Answer: B
Explanation:
Using separate Amazon Redshift clusters for ETL and analytics isolates write-heavy ETL activity from read-heavy analytical workloads, which eliminates the lock contention that is causing timeouts and deadlocks. Redshift data sharing provides near real-time, transactionally consistent access to the same data across clusters, so analysts can query a stable view of the data while ETL processes continue independently.
NEW QUESTION # 87
A retail company stores point-of-sale transaction data in an Amazon RDS for MySQL database.
The company maintains historical sales analytics in Amazon Redshift. The company needs to create daily reports that combine the current day's transactions with historical sales patterns for trend analysis. The company requires a solution that provides near real-time insights while minimizing data transfer costs and maintenance overhead.
Which solution will meet these requirements?
- A. Configure AWS Database Migration Service (AWS DMS) to continuously replicate data from RDS for MySQL to Amazon Redshift. Use Redshift queries to create consolidated reports.
- B. Implement Amazon Redshift federated queries to directly access RDS for MySQL data and join it with existing Redshift tables in a single query.
- C. Use AWS Glue to create an extract, transform, and load (ETL) pipeline that runs every hour to copy incremental data from RDS for MySQL to Amazon Redshift. Generate reports.
- D. Export RDS for MySQL data to an Amazon S3 bucket on a regular schedule. Use the COPY command to load the data into Amazon Redshift staging tables. Join the data with historical data.
Answer: A
Explanation:
AWS DMS with ongoing replication (CDC) continuously streams new RDS for MySQL transactions into Amazon Redshift, providing near real-time freshness with minimal maintenance and transferring only changes instead of full batches. Reports can then run in Redshift joining current and historical data without hourly ETL jobs or S3 staging.
NEW QUESTION # 88
A company has an Amazon S3 based data lake. The data lake contains datasets that belong to multiple departments. The data lake ingests millions of customer records each day.
A data engineer needs to design an access and storage solution that allows departments to access only the subset of the company's dataset that each department requires. The solution must follow the principle of least privilege.
Which solution will meet these requirements with the LEAST operational effort?
- A. Define IAM policies and IAM roles for each department. Specify the S3 access paths from the data lake that each team can access.
- B. Set up Amazon Redshift and Amazon Redshift Spectrum as the primary entry points for the data lake. Define an IAM role that Amazon Redshift can assume. Configure the IAM role to grant access to the data that is in Amazon S3.
- C. Deploy an Amazon RDS for PostgreSQL database that has the aws_s3 extension installed.Configure AWS Step Functions events to invoke an AWS Lambda function to sync the data lake with the database.
- D. Set up AWS Lake Formation. Assign LF-Tags to AWS Glue Data Catalog resources. Enable Lake Formation tag-based access control (LF-TBAC).
Answer: D
Explanation:
AWS Lake Formation with LF-Tags and LF-TBAC is designed for fine-grained, scalable access control across shared data lake resources. It lets the company grant each department access only to the datasets it needs while following least privilege, and it reduces ongoing administrative work compared with managing separate IAM policies and S3 paths for every team.
NEW QUESTION # 89
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