Google ADP Value Pack (Frequently Bought Together)

Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

• A. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
• B. Migrate the Spark jobs to Dataproc Serverless.
• C. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
• D. Migrate the Spark jobs to Dataproc on Compute Engine.

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

You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

• A. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
• B. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
• C. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
• D. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.

You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

• A. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
• B. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
• C. Use Cloud Composer to orchestrate the Spark job and email the report.
• D. Use Cloud Run functions to trigger the Spark job and email the report.

You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

• A. Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
• B. Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.
• C. Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
• D. Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.