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NEW QUESTION # 280
Which stages support external tables?
Answer: B
Explanation:
Explanation
External stages only from any region, and any cloud provider support external tables. External tables are virtual tables that can query data from files stored in external stages without loading them into Snowflake tables. External stages are references to locations outside of Snowflake, such as Amazon S3 buckets, Azure Blob Storage containers, or Google Cloud Storage buckets. External stages can be created from any region and any cloud provider, as long as they have a valid URL and credentials. The other options are incorrect because internal stages do notsupport external tables. Internal stages are locations within Snowflake that can store files for loading or unloading data. Internal stages can be user stages, table stages, or named stages.
NEW QUESTION # 281
A company uses AWS Glue Data Catalog to index data that is uploaded to an Amazon S3 bucket every day. The company uses a daily batch processes in an extract, transform, and load (ETL) pipeline to upload data from external sources into the S3 bucket.
The company runs a daily report on the S3 data. Some days, the company runs the report before all the daily data has been uploaded to the S3 bucket. A data engineer must be able to send a message that identifies any incomplete data to an existing Amazon Simple Notification Service (Amazon SNS) topic.
Which solution will meet this requirement with the LEAST operational overhead?
Answer: D
Explanation:
AWS Glue workflows and data quality actions allow for seamless integration with AWS Glue to ensure that data quality checks are automatically performed after ETL jobs. This solution leverages the existing AWS Glue infrastructure and EventBridge to trigger notifications without needing complex orchestration tools like Apache Airflow or EMR, resulting in lower operational overhead.
Amazon EventBridge can easily be configured to trigger a notification to Amazon SNS when a dataset is incomplete, providing a highly automated and low-maintenance solution.
Airflow requires managing a separate cluster and configuring Directed Acyclic Graphs (DAGs), which introduces more operational complexity and overhead than AWS Glue workflows.
Using Amazon EMR introduces unnecessary complexity and higher operational overhead, as it requires provisioning and managing a cluster for Spark jobs, which is more complex than using AWS Glue and EventBridge.
While Lambda can be a lightweight option, orchestrating Lambda functions for data quality checks with Step Functions adds unnecessary complexity when AWS Glue workflows already offer built-in capabilities for data quality actions with lower overhead.
NEW QUESTION # 282
A company needs to load customer data that comes from a third party into an Amazon Redshift data warehouse. The company stores order data and product data in the same data warehouse.
The company wants to use the combined dataset to identify potential new customers.
A data engineer notices that one of the fields in the source data includes values that are in JSON format.
How should the data engineer load the JSON data into the data warehouse with the LEAST effort?
Answer: A
Explanation:
The SUPER data type in Amazon Redshift allows you to natively store semi-structured data such as JSON. By using the SUPER data type, you can store JSON data in a Redshift table without needing to flatten or transform it beforehand. This approach requires the least effort because it allows you to directly load the JSON data and query it using Redshift's JSONPath and PARTITION BY capabilities without additional ETL processing.
NEW QUESTION # 283
A data engineer creates an AWS Lambda function that an Amazon EventBridge event will invoke.
When the data engineer tries to invoke the Lambda function by using an EventBridge event, an AccessDeniedException message appears.
How should the data engineer resolve the exception?
Answer: B
Explanation:
The lambda resource based policy must allow the events principle to invoke the lambda function.
Amazon SQS, Amazon SNS, Lambda, CloudWatch Logs, and EventBridge bus targets do not use roles, and permissions to EventBridge must be granted via a resource policy.
https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-run-lambda-schedule.html#eb- schedule-create-rule
https://docs.aws.amazon.com/eventbridge/latest/userguide/eb-run-lambda-schedule.html#eb- schedule-create-rule
NEW QUESTION # 284
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.)
Answer: A,D
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 # 285
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