google - associate data practitioner pt 2

You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

• .Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.

• .Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.

• Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.

• .Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.

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?

• .Migrate the Spark jobs to Dataproc Serverless.

• .Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs

• Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.

• .Migrate the Spark jobs to Dataproc on Compute Engine.

You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

• .Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery

• .Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.

• .Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage

• .Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.

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?

• .Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.

• 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.

• Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.

• Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data

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?

• .Use Cloud Scheduler to schedule the jobs to run.

• .Use Cloud Tasks to schedule and run the jobs asynchronously

• .Create directed acyclic graphs (DAGs) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery

• .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.

You are responsible for managing Cloud Storage buckets for a research company. Your company has well-defined data tiering and retention rules. You need to optimize storage costs while achieving your data retention needs. What should you do?

• .Configure the buckets to use the Archive storage class.

• .Configure a lifecycle management policy on each bucket to downgrade the storage class and remove objects based on age

• .Configure the buckets to use the Standard storage class and enable Object Versioning.

• .Configure the buckets to use the Autoclass feature.

You are using your own data to demonstrate the capabilities of BigQuery to your organization’s leadership team. You need to perform a one- time load of the files stored on your local machine into BigQuery using as little effort as possible. What should you do?

• .Write and execute a Python script using the BigQuery Storage Write API library.

• Create a Dataproc cluster, copy the files to Cloud Storage, and write an Apache Spark job using the spark

• .Execute the bq load command on your local machine.

• .Create a Dataflow job using the Apache Beam FileIO and BigQueryIO connectors with a local runner.

Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

• Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.

• .Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.

• Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.

• .Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.

Your company uses Looker to generate and share reports with various stakeholders. You have a complex dashboard with several visualizations that needs to be delivered to specific stakeholders on a recurring basis, with customized filters applied for each recipient. You need an efficient and scalable solution to automate the delivery of this customized dashboard. You want to follow the Google-recommended approach. What should you do?

• Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.

• .Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.

• .Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.

• .Use the Looker Scheduler with a user attribute filter on the dashboard, and send the dashboard with personalized filters to each stakeholder based on their attributes.

You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

• .Export the data from BigQuery to a local machine. Use scikit-learn in a Jupyter notebook to build the churn prediction model.

• .Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.

• .Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn

• Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.