You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

• A. Use the gcloud storage command to transfer the data over the internet.
• B. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
• C. Use Cloud Client Libraries to transfer the data over the internet.
• D. Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.

Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one- time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

• A. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
• B. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
• C. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
• D. Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.

Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

• A. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
• B. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
• C. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
• D. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.

Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

• A. Create an external table.
• B. Create an object table.
• C. Create a native table.
• D. Create a temporary table.

You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

• A. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
• B. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
• C. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
• D. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.