Professional-Data-Engineer 무료 덤프문제 온라인 액세스

You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

• A.Convert the streaming insert code to batch load for individual messages.
• B.Re-write the application to load accumulated data every 2 minutes.
• C.Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
• D.Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

You are designing a cloud-native historical data processing system to meet the following conditions:
- The data being analyzed is in CSV, Avro, and PDF formats and will be
accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
- A streaming data pipeline stores new data daily.
- Peformance is not a factor in the solution.
- The solution design should maximize availability.
How should you design data storage for this solution?

• A.Store the data in BigQuery. Access the data using the BigQuery Connector on Cloud Dataproc and Compute Engine.
• B.Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.
• C.Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.
• D.Store the data in a regional Cloud Storage bucket. Access the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.

You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?

• A.Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences.
  Deploy the models using Cloud Dataproc. Call the models from your application.
• B.Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
• C.Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.
• D.Build and train a complex classification model with Spark MLlib to generate labels and filter the results. Deploy the models using Cloud Dataproc. Call the model from your application.

The Development and External teams have the project viewer Identity and Access Management (IAM) role in a folder named Visualization. You want the Development Team to be able to read data from both Cloud Storage and BigQuery, but the External Team should only be able to read data from BigQuery. What should you do?

• A.Remove Cloud Storage IAM permissions to the External Team on the acme-raw-data project
• B.Create Virtual Private Cloud (VPC) firewall rules on the acme-raw-data protect that deny all Ingress traffic from the External Team CIDR range
• C.Create a VPC Service Controls perimeter containing both protects and BigQuery as a restricted API Add the External Team users to the perimeter s Access Level
• D.Create a VPC Service Controls perimeter containing both protects and Cloud Storage as a restricted API. Add the Development Team users to the perimeter's Access Level

You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

• A.Use Cloud Vision AutoML, but reduce your dataset twice.
• B.Train your own image recognition model leveraging transfer learning techniques.
• C.Use Cloud Vision API by providing custom labels as recognition hints.
• D.Use Cloud Vision AutoML with the existing dataset.