Professional-Data-Engineer 試験問題を無料オンラインアクセス

Which of the following are feature engineering techniques? (Select 2 answers)

• A.Feature prioritization
• B.Hidden feature layers
• C.Bucketization of a continuous feature
• D.Crossed feature columns

Your organization stores customer data in an on-premises Apache Hadoop cluster in Apache Parquet format. Data is processed on a daily basis by Apache Spark jobs that run on the cluster.
You are migrating the Spark jobs and Parquet data to Google Cloud. BigQuery will be used on future transformation pipelines so you need to ensure that your data is available in BigQuery. You want to use managed services, while minimizing ETL data processing changes and overhead costs. What should you do?

• A.Migrate your data to Cloud Storage and register the bucket as a Dataplex asset. Refactor Spark pipelines to write and read data on Cloud Storage, and run them on Dataproc Serverless.
• B.Migrate your data to BigQuery. Refactor Spark pipelines to write and read data on BigQuery, and run them on Dataproc Serverless.
• C.Migrate your data to Cloud Storage and migrate the metadata to Dataproc Metastore (DPMS).
  Refactor Spark pipelines to write and read data on Cloud Storage, and run them on Dataproc Serverless.
• D.Migrate your data to BigLake. Refactor Spark pipelines to write and read data on Cloud Storage, and run them on Dataproc on Compute Engine.

You are migrating your on-premises data warehouse to BigQuery. As part of the migration, you want to facilitate cross-team collaboration to get the most value out of the organization's data.
You need to design an architecture that would allow teams within the organization to securely publish, discover, and subscribe to read-only data in a self-service manner. You need to minimize costs while also maximizing data freshness. What should you do?

• A.Use Analytics Hub to facilitate data sharing.
• B.Create a new dataset for sharing in each individual team's project. Grant the subscribing team the bigquery dataViewer role on the dataset.
• C.Create authorized datasets to publish shared data in the subscribing team's project.
• D.Use BigQuery Data Transfer Service to copy datasets to a centralized BigQuery project for sharing.

A TensorFlow machine learning model on Compute Engine virtual machines (n2-standard -32) takes two days to complete framing. The model has custom TensorFlow operations that must run partially on a CPU. You want to reduce the training time in a cost-effective manner. What should you do?

• A.Change the VM type to e2 standard-32
• B.Train the model using a VM with a GPU hardware accelerator
• C.Train the model using a VM with a TPU hardware accelerator
• D.Change the VM type to n2-highmem-32

You have 15 TB of data in your on-premises data center that you want to transfer to Google Cloud. Your data changes weekly and is stored in a POSIX-compliant source. The network operations team has granted you 500 Mbps bandwidth to the public internet. You want to follow Google recommended practices to reliably transfer your data to Google Cloud on a weekly basis.
What should you do?

• A.Install Storage Transfer Service for on-premises data on a Google Cloud virtual machine, and then configure a weekly transfer job.
• B.Use Transfer Appliance to migrate your data into a Google Kubernetes Engine cluster, and then configure a weekly transfer job.
• C.Use Cloud Scheduler to trigger the gsutil command. Use the -m parameter for optimal parallelism.
• D.Install Storage Transfer Service for on-premises data in your data center, and then configure a weekly transfer job.