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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q69-Q74):
NEW QUESTION # 69
A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.
The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.
Which solution will meet these requirements with the LOWEST latency?
- A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream database. Use the Timestream database as a source to create an Amazon QuickSight dashboard.
- B. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard.
- C. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
- D. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.
Answer: D
Explanation:
Amazon Managed Service for Apache Flink provides real-time stream processing capabilities, which can process sensor data with low latency. By using Apache Flink connectors, the processed data can be efficiently written to Amazon Timestream, which is optimized for time- series data storage and querying.
NEW QUESTION # 70
By default, a newly-created Custom role is not assigned to any user, nor granted to any other role?
- A. TRUE
- B. FALSE
Answer: A
NEW QUESTION # 71
A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.
A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Load data into Amazon Kinesis Data Firehose. Load the data into Amazon Redshift.
- B. Use the Amazon Aurora zero-ETL integration with Amazon Redshift.
- C. Use the streaming ingestion feature of Amazon Redshift.
- D. Load the data into Amazon S3. Use the COPY command to load the data into Amazon Redshift.
Answer: C
Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-streaming-ingestion.html Use the Streaming Ingestion Feature of Amazon Redshift: Amazon Redshift recently introduced streaming data ingestion, allowing Redshift to consume data directly from Kinesis Data Streams in near real-time. This feature simplifies the architecture by eliminating the need for intermediate steps or services, and it is specifically designed to support near real-time analytics. The operational overhead is minimal since the feature is integrated within Redshift.
NEW QUESTION # 72
A Data Engineer is investigating a query that is taking a long time to return The Query Profile shows the following:
What step should the Engineer take to increase the query performance?
- A. Add additional virtual warehouses.
- B. Change the order of the joins and start with smaller tables first
- C. increasethe size of the virtual warehouse.
- D. Rewrite the query using Common Table Expressions (CTEs)
Answer: C
Explanation:
Explanation
The step that the Engineer should take to increase the query performance is to increase the size of the virtual warehouse. The Query Profile shows that most of the time was spent on local disk IO, which indicates that the query was reading a lot of data from disk rather than from cache. This could be due to a large amount of data being scanned or a low cache hit ratio. Increasing the size of the virtual warehouse will increase the amount of memory and cache available for the query, which could reduce the disk IO time and improve the query performance. The other options are not likely to increase the query performance significantly. Option A, adding additional virtual warehouses, will not help unless they are used in a multi-cluster warehouse configuration or for concurrent queries. Option C, rewriting the query using Common Table Expressions (CTEs), will not affect the amount of data scanned or cached by the query. Option D, changing the order of the joins and starting with smaller tables first, will not reduce the disk IO time unless it also reduces the amount of data scanned or cached by the query.
NEW QUESTION # 73
A company has a data lake on AWS. The data lake ingests sources of data from business units.
The company uses Amazon Athena for queries. The storage layer is Amazon S3 with an AWS Glue Data Catalog as a metadata repository.
The company wants to make the data available to data scientists and business analysts.
However, the company first needs to manage fine-grained, column-level data access for Athena based on the user roles and responsibilities.
Which solution will meet these requirements?
- A. Create a resource share in AWS Resource Access Manager (AWS RAM) to grant access to IAM users.
- B. Define an IAM identity-based policy for AWS Glue tables. Attach the same policy to IAM roles.
Associate the IAM roles with IAM groups that contain the users. - C. Set up AWS Lake Formation. Define security policy-based rules for the users and applications by IAM role in Lake Formation.
- D. Define an IAM resource-based policy for AWS Glue tables. Attach the same policy to IAM user groups.
Answer: C
Explanation:
Lake Formation supports fine-grained access control, including column-level permissions.
NEW QUESTION # 74
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