Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?

• A. Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
• B. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
• C. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.
• D. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.

You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage.
The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

• A. ETLT
• B. EL
• C. ELT
• D. ETL

Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?

• A. Apply a filter to only show products with a positive profit margin.
• B. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
• C. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
• D. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.

Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

• A. Use Cloud Scheduler to schedule the jobs to run.
• B. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
• C. Use Cloud Tasks to schedule and run the jobs asynchronously.
• D. Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

• A. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
• B. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
• C. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
• D. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.