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?

Define a new measure that calculates the profit margin by using the existing revenue and cost fields.

Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.

Define a new measure that calculates the profit margin by using the existing revenue and cost fields.

Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

Apply a filter to only show products with a positive profit margin.

You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

Export the data to Cloud Storage, and use signed URLs to authorize access.

Enable access control by using IAM roles.

Encrypt the data by using customer-managed encryption keys (CMEK).

Update dataset privileges by using the SQL GRANT statement.

Export the data to Cloud Storage, and use signed URLs to authorize access.

Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?

Use the ''Pub/Sub to BigQuery'' Dataflow template with a UDF, and write the results to BigQuery.

Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.

Use a Pub/Sub to Cloud Storage subscription, write a Cloud Run service that is triggered when objects arrive in the bucket, performs the transformations, and writes the results to BigQuery.

Use the ''Pub/Sub to BigQuery'' Dataflow template with a UDF, and write the results to BigQuery.

Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.

You want to process and load a daily sales CSV file stored in Cloud Storage into BigQuery for downstream reporting. You need to quickly build a scalable data pipeline that transforms the data while providing insights into data quality issues. What should you do?

Create a batch pipeline in Cloud Data Fusion by using a Cloud Storage source and a BigQuery sink.

Load the CSV file as a table in BigQuery, and use scheduled queries to run SQL transformation scripts.

Load the CSV file as a table in BigQuery. Create a batch pipeline in Cloud Data Fusion by using a BigQuery source and sink.

Create a batch pipeline in Dataflow by using the Cloud Storage CSV file to BigQuery batch template.

You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.

Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.

Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.

Develop a batch process using Dataflow that runs weekly and deletes files based on their age.

Create a Cloud Run function that runs daily and deletes files older than seven days.

You work for a healthcare company that has a large on-premises data system containing patient records with personally identifiable information (PII) such as names, addresses, and medical diagnoses. You need a standardized managed solution that de-identifies PII across all your data feeds prior to ingestion to Google Cloud. What should you do?

Use Cloud Data Fusion to transform the data. Store the cleaned data in BigQuery.

Use Cloud Run functions to create a serverless data cleaning pipeline. Store the cleaned data in BigQuery.

Use Cloud Data Fusion to transform the data. Store the cleaned data in BigQuery.

Load the data into BigQuery, and inspect the data by using SQL queries. Use Dataflow to transform the data and remove any errors.

Use Apache Beam to read the data and perform the necessary cleaning and transformation operations. Store the cleaned data in BigQuery.

You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types. You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?

Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.

Configure lifecycle management rules to transition objects to appropriate storage classes based on access patterns. Set up Object Versioning for all objects to meet immutability requirements.

Move objects to different storage classes based on their age and access patterns. Use Cloud Key Management Service (Cloud KMS) to encrypt specific objects with customer-managed encryption keys (CMEK) to meet immutability requirements.

Create a Cloud Run function to periodically check object metadata, and move objects to the appropriate storage class based on age and access patterns. Use object holds to enforce immutability for specific objects.

Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.

You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

Use BigQuery ML to create a logistic regression model for purchase prediction.

Use Vertex AI Workbench to develop a custom model for purchase prediction.

Use Colab Enterprise to develop a custom model for purchase prediction.

Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.

You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.

Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.

Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.

Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.

Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.

Another team in your organization is requesting access to a BigQuery dataset. You need to share the dataset with the team while minimizing the risk of unauthorized copying of data. You also want to create a reusable framework in case you need to share this data with other teams in the future. What should you do?

Create a private exchange using Analytics Hub with data egress restriction, and grant access to the team members.

Create authorized views in the team's Google Cloud project that is only accessible by the team.

Create a private exchange using Analytics Hub with data egress restriction, and grant access to the team members.

Enable domain restricted sharing on the project. Grant the team members the BigQuery Data Viewer IAM role on the dataset.

Export the dataset to a Cloud Storage bucket in the team's Google Cloud project that is only accessible by the team.

Your company has developed a website that allows users to upload and share video files. These files are most frequently accessed and shared when they are initially uploaded. Over time, the files are accessed and shared less frequently, although some old video files may remain very popular.You need to design a storage system that is simple and cost-effective. What should you do?

Create a single-region bucket with custom Object Lifecycle Management policies based on upload date.

Create a single-region bucket with Autoclass enabled.

Create a single-region bucket. Configure a Cloud Scheduler job that runs every 24 hours and changes the storage class based on upload date.

Create a single-region bucket with custom Object Lifecycle Management policies based on upload date.

Create a single-region bucket with Archive as the default storage class.

You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege. What should you do?

Request the Dataflow Developer role.

Request the Dataflow Viewer role.

Request the Dataflow Worker role.

Request the Dataflow Admin role.

You need to create a new data pipeline. You want a serverless solution that meets the following requirements:* Data is streamed from Pub/Sub and is processed in real-time.* Data is transformed before being stored.* Data is stored in a location that will allow it to be analyzed with SQL using Looker. Which Google Cloud services should you recommend for the pipeline?

1. Dataproc Serverless 2. Bigtable

1. Cloud Composer 2. Cloud SQL for MySQL

Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

Create a materialized view in BigQuery that uses the SUM( ) function and the DATE_SUB( ) function.

Create a saved query in the BigQuery console that uses the SUM( ) function and the DATE_SUB( ) function. Re-run the saved query every month, and save the results to a BigQuery table.

Create a BigQuery table that uses the SUM( ) function and the _PARTITIONDATE filter.

Create a BigQuery table that uses the SUM( ) function and the DATE_DIFF( ) function.

You have a BigQuery dataset containing sales data. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.

Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.

Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.

Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.

Store all data in a single BigQuery table without partitioning or lifecycle policies.

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?

Create a sales_region user attribute, and assign each manager's region as the value of their user attribute. Add an access_filter Explore filter on the region_name dimension by using the sales_region user attribute.

Create five different Explores with the sql_always_filter Explore filter applied on the region_name dimension. Set each region_name value to the corresponding region for each manager.

Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.

Create separate Looker instances for each regional manager. Copy the LookML model and dashboard to each instance. Provision viewer access to the corresponding manager.

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 to clean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.

Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.

Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.

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.

Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.

Your retail organization stores sensitive application usage data in Cloud Storage. You need to encrypt the data without the operational overhead of managing encryption keys. What should you do?

Use Google-managed encryption keys (GMEK).

Use customer-managed encryption keys (CMEK).

Use customer-supplied encryption keys (CSEK).

Use customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.

You work for a financial organization that stores transaction data in BigQuery. Your organization has a regulatory requirement to retain data for a minimum of seven years for auditing purposes. You need to ensure that the data is retained for seven years using an efficient and cost-optimized approach. What should you do?

Set the table-level retention policy in BigQuery to seven years.

Create a partition by transaction date, and set the partition expiration policy to seven years.

Set the table-level retention policy in BigQuery to seven years.

Set the dataset-level retention policy in BigQuery to seven years.

Export the BigQuery tables to Cloud Storage daily, and enforce a lifecycle management policy that has a seven-year retention rule.

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

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?

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

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

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?

Execute the bq load command on your local machine.

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-bigquery-connector.

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?

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

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?

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.

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?

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.

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.

You are a data analyst at your organization. You have been given a BigQuery dataset that includes customer information. The dataset contains inconsistencies and errors, such as missing values, duplicates, and formatting issues. You need to effectively and quickly clean the data. What should you do?

Use BigQuery's built-in functions to perform data quality transformations.

Develop a Dataflow pipeline to read the data from BigQuery, perform data quality rules and transformations, and write the cleaned data back to BigQuery.

Use Cloud Data Fusion to create a data pipeline to read the data from BigQuery, perform data quality transformations, and write the clean data back to BigQuery.

Export the data from BigQuery to CSV files. Resolve the errors using a spreadsheet editor, and re-import the cleaned data into BigQuery.

Use BigQuery's built-in functions to perform data quality transformations.

Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department. What should you do?

Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.

Create a custom quota for each analyst in BigQuery.

Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.

Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.

Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.

You manage a web application that stores data in a Cloud SQL database. You need to improve the read performance of the application by offloading read traffic from the primary database instance. You want to implement a solution that minimizes effort and cost. What should you do?

Enable automatic backups, and create a read replica of the Cloud SQL instance.

Use Cloud CDN to cache frequently accessed data.

Store frequently accessed data in a Memorystore instance.

Migrate the database to a larger Cloud SQL instance.

Enable automatic backups, and create a read replica of the Cloud SQL instance.

Your organization plans to move their on-premises environment to Google Cloud. Your organization's network bandwidth is less than 1 Gbps. You need to move over 500 of data to Cloud Storage securely, and only have a few days to move the data. What should you do?

Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.

Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.

Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.

Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.

Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.

Create a scheduled query that periodically runs an update statement in SQL that sets the ''deleted' column to ''yes'' for data that is more than one year old. Create a view that filters out rows that have been marked deleted.

Create a view that filters out rows that are older than one year.

Require users to specify a partition filter using the alter table statement in SQL.

Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.

Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

Use Cloud Data Fusion pipelines.

You manage a BigQuery table that is used for critical end-of-month reports. The table is updated weekly with new sales data. You want to prevent data loss and reporting issues if the table is accidentally deleted. What should you do?

Schedule the creation of a new snapshot of the table once a week. On deletion, re-create the deleted table using the snapshot and time travel data.

Configure the time travel duration on the table to be exactly seven days. On deletion, re-create the deleted table solely from the time travel data.

Schedule the creation of a new snapshot of the table once a week. On deletion, re-create the deleted table using the snapshot and time travel data.

Create a clone of the table. On deletion, re-create the deleted table by copying the content of the clone.

Create a view of the table. On deletion, re-create the deleted table from the view and time travel data.

Your organization sends IoT event data to a Pub/Sub topic. Subscriber applications read and perform transformations on the messages before storing them in the data warehouse. During particularly busy times when more data is being written to the topic, you notice that the subscriber applications are not acknowledging messages within the deadline. You need to modify your pipeline to handle these activity spikes and continue to process the messages. What should you do?

Implement flow control on the subscribers

Retry messages until they are acknowledged.

Implement flow control on the subscribers

Forward unacknowledged messages to a dead-letter topic.

Seek back to the last acknowledged message.

You have millions of customer feedback records stored in BigQuery. You want to summarize the data by using the large language model (LLM) Gemini. You need to plan and execute this analysis using the most efficient approach. What should you do?

Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use Gemini to summarize the data.

Query the BigQuery table from within a Python notebook, use the Gemini API to summarize the data within the notebook, and store the summaries in BigQuery.

Use a BigQuery ML model to pre-process the text data, export the results to Cloud Storage, and use the Gemini API to summarize the pre- processed data.

Create a BigQuery Cloud resource connection to a remote model in Vertex Al, and use Gemini to summarize the data.

Export the raw BigQuery data to a CSV file, upload it to Cloud Storage, and use the Gemini API to summarize the data.

You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of data. What should you do?

Use Dataflow to create a streaming pipeline that includes validation and transformation steps.

Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.

Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.

Use Dataflow to create a streaming pipeline that includes validation and transformation steps.

Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.

Google Associate Data Practitioner Practice Test 1 - Free Online Exam Prep & Questions

Question 1 / 40

Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

Use a Transfer Appliance.

Use Cloud Storage FUSE.

Use Storage Transfer Service.

Use gcloud storage commands.

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