Your organization needs to store historical customer order data. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

Store the data in Cloud Storaqe usinq Nearline storaqe.

Store the data in Cloud Storaqe usinq Coldline storaqe.

Store the data in Cloud Storage using Standard storage.

Store the data in Cloud Storage using Archive storage.

You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.

Use Cloud Logging to identify error groups in the pipeline's logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.

Use Cloud Logging to create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.

Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.

Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline's metrics, such as CPU utilization and memory usage.

Your organization's business analysts require near real-time access to streaming data. However, they are reporting that their dashboard queries are loading slowly. After investigating BigQuery query performance, you discover the slow dashboard queries perform several joins and aggregations.You need to improve the dashboard loading time and ensure that the dashboard data is as up-to-date as possible. What should you do?

Disable BiqQuery query result caching.

Modify the schema to use parameterized data types.

Create a scheduled query to calculate and store intermediate results.

You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.

Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.

Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.

Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.

You work for an online retail company. Your company collects customer purchase data in CSV files and pushes them to Cloud Storage every 10 minutes. The data needs to be transformed and loaded into BigQuery for analysis. The transformation involves cleaning the data, removing duplicates, and enriching it with product information from a separate table in BigQuery. You need to implement a low-overhead solution that initiates data processing as soon as the files are loaded into Cloud Storage. What should you do?

Use Dataflow to implement a streaming pipeline using an OBJECT_FINALIZE notification from Pub/Sub to read the data from Cloud Storage, perform the transformations, and write the data to BigQuery.

Use Cloud Composer sensors to detect files loading in Cloud Storage. Create a Dataproc cluster, and use a Composer task to execute a job on the cluster to process and load the data into BigQuery.

Schedule a direct acyclic graph (DAG) in Cloud Composer to run hourly to batch load the data from Cloud Storage to BigQuery, and process the data in BigQuery using SQL.

Use Dataflow to implement a streaming pipeline using an OBJECT_FINALIZE notification from Pub/Sub to read the data from Cloud Storage, perform the transformations, and write the data to BigQuery.

Create a Cloud Data Fusion job to process and load the data from Cloud Storage into BigQuery. Create an OBJECT_FINALI ZE notification in Pub/Sub, and trigger a Cloud Run function to start the Cloud Data Fusion job as soon as new files are loaded.

You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re-run risk reports when new data becomes available. What should you do?

Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.

Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.

Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.

Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.

Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.

Your organization stores highly personal data in BigQuery and needs to comply with strict data privacy regulations. You need to ensure that sensitive data values are rendered unreadable whenever an employee leaves the organization. What should you do?

Use customer-managed encryption keys (CMEK) and delete keys when employees leave the organization.

Use AEAD functions and delete keys when employees leave the organization.

Use dynamic data masking and revoke viewer permissions when employees leave the organization.

Use customer-managed encryption keys (CMEK) and delete keys when employees leave the organization.

Use column-level access controls with policy tags and revoke viewer permissions when employees leave the organization.

You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model. What should you do?

Compare the two different models.

Your company uses Looker to visualize and analyze sales data. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

Create a single Explore with all sales metrics. Build the dashboard using this Explore.

Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.

Create a single Explore with all sales metrics. Build the dashboard using this Explore.

Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.

Your company's ecommerce website collects product reviews from customers. The reviews are loaded as CSV files daily to a Cloud Storage bucket. The reviews are in multiple languages and need to be translated to Spanish. You need to configure a pipeline that is serverless, efficient, and requires minimal maintenance. What should you do?

Load the data into BigQuery using a Cloud Run function. Create a BigQuery remote function that invokes the Cloud Translation API. Use a scheduled query to translate new reviews.

Load the data into BigQuery using Dataproc. Use Apache Spark to translate the reviews by invoking the Cloud Translation API. Set BigQuery as the sink.U

Use a Dataflow templates pipeline to translate the reviews using the Cloud Translation API. Set BigQuery as the sink.

Load the data into BigQuery using a Cloud Run function. Use the BigQuery ML create model statement to train a translation model. Use the model to translate the product reviews within BigQuery.

Load the data into BigQuery using a Cloud Run function. Create a BigQuery remote function that invokes the Cloud Translation API. Use a scheduled query to translate new reviews.

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?

Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.

Use Cloud Composer to orchestrate the Spark job and email the report.

Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.

Use Cloud Run functions to trigger the Spark job and email the report.

Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.

Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive data. What should you do?

Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.

Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.

Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.

Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.

Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.

You are a database administrator managing sales transaction data by region stored in a BigQuery table. You need to ensure that each sales representative can only see the transactions in their region. What should you do?

Create a row-level access policy.

Grant the appropriate 1AM permissions on the dataset.

You work for a financial services company that handles highly sensitive data. Due to regulatory requirements, your company is required to have complete and manual control of data encryption. Which type of keys should you recommend to use for data storage?

Use customer-supplied encryption keys (CSEK).

Use a dedicated third-party key management system (KMS) chosen by the company.

Use Google-managed encryption keys (GMEK).

Use customer-managed encryption keys (CMEK).

Your team needs to analyze large datasets stored in BigQuery to identify trends in user behavior. The analysis will involve complex statistical calculations, Python packages, and visualizations. You need to recommend a managed collaborative environment to develop and share the analysis. What should you recommend?

Create a Colab Enterprise notebook and connect the notebook to BigQuery. Share the notebook with your team. Analyze the data and generate visualizations in Colab Enterprise.

Create a statistical model by using BigQuery ML. Share the query with your team. Analyze the data and generate visualizations in Looker Studio.

Create a Looker Studio dashboard and connect the dashboard to BigQuery. Share the dashboard with your team. Analyze the data and generate visualizations in Looker Studio.

Connect Google Sheets to BigQuery by using Connected Sheets. Share the Google Sheet with your team. Analyze the data and generate visualizations in Gooqle Sheets.

Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner's data in its own BigQuery dataset. Each partner should be able to access only their data. You want to share the data while following Google-recommended practices. What should you do?

Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.

Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub/Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.

Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.

Grant the partners the bigquery.user IAM role on the BigQuery project.

Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?

Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.

Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.

Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.

Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.

Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.

Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.

Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.

Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job ID.

Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.

Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.

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

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?

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

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 team uses the Google Ads platform to visualize metrics. You want to export the data to BigQuery to get more granular insights. You need to execute a one-time transfer of historical data and automatically update data daily. You want a solution that is low-code, serverless, and requires minimal maintenance. What should you do?

Export the historical data to BigQuery by using BigQuery Data Transfer Service. Use BigQuery Data Transfer Service for daily automation.

Export the historical data to BigQuery by using BigQuery Data Transfer Service. Use Cloud Composer for daily automation.

Export the historical data to Cloud Storage by using Storage Transfer Service. Use Pub/Sub to trigger a Dataflow template that loads data for daily automation.

Export the historical data as a CSV file. Import the file into BigQuery for analysis. Use Cloud Composer for daily automation.

Export the historical data to BigQuery by using BigQuery Data Transfer Service. Use BigQuery Data Transfer Service for daily automation.

Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive data. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?

Use authorized views to share query results with the payroll specialist.

Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.

Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.

Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.

You work for a global financial services company that trades stocks 24/7. You have a Cloud SGL for PostgreSQL user database. You need to identify a solution that ensures that the database is continuously operational, minimizes downtime, and will not lose any data in the event of a zonal outage. What should you do?

Configure and create a high-availability Cloud SQL instance with the primary instance in zone A and a secondary instance in any zone other than zone A.

Continuously back up the Cloud SGL instance to Cloud Storage. Create a Compute Engine instance with PostgreSCL in a different region. Restore the backup in the Compute Engine instance if a failure occurs.

Create a read replica in another region. Promote the replica to primary if a failure occurs.

Configure and create a high-availability Cloud SQL instance with the primary instance in zone A and a secondary instance in any zone other than zone A.

Create a read replica in the same region but in a different zone.

You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional data. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.

Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.

Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.

Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.

Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.

You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:1. Audio files from phone interactions with support agents that will be accessed during trainings.2. CSV files of users' personally identifiable information (Pll) that will be analyzed with SQL.3. A large volume of small document files that will power other applications.You need to select the appropriate tool for each data type given the required use case, while following Google-recommended practices. Which should you choose?

1. Cloud Storage 2. BigQuery 3. Firestore

1. Cloud Storage 2. CloudSQL for PostgreSQL 3. Bigtable

1. Filestore 2. Cloud SQL for PostgreSQL 3. Datastore

1. Cloud Storage 2. BigQuery 3. Firestore

1. Filestore 2. Bigtable 3. BigQuery

You are a Looker analyst. You need to add a new field to your Looker report that generates SQL that will run against your company's database. You do not have the Develop permission. What should you do?

Create a custom field from the field picker in Looker, and add it to your report.

Create a new field in the LookML layer, refresh your report, and select your new field from the field picker.

Create a calculated field using the Add a field option in Looker Studio, and add it to your report.

Create a table calculation from the field picker in Looker, and add it to your report.

Create a custom field from the field picker in Looker, and add it to your report.

Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.

Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.

Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.

Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting

You are constructing a data pipeline to process sensitive customer data stored in a Cloud Storage bucket. You need to ensure that this data remains accessible, even in the event of a single-zone outage. What should you do?

Store the data in a multi-region bucket.

Set up a Cloud CDN in front of the bucket.

Enable Object Versioning on the bucket.

Store the data in a multi-region bucket.

Store the data in Nearline storaqe.

Your retail company collects customer data from various sources:You are designing a data pipeline to extract this data. Which Google Cloud storage system(s) should you select for further analysis and ML model training?

1. Online transactions: BigQuery 2. Customer feedback: Cloud Storage 3. Social media activity: BigQuery

1. Online transactions: Cloud Storage 2. Customer feedback: Cloud Storage 3. Social media activity: Cloud Storage

1. Online transactions: BigQuery 2. Customer feedback: Cloud Storage 3. Social media activity: BigQuery

1. Online transactions: Bigtable 2. Customer feedback: Cloud Storage 3. Social media activity: CloudSQL for MySQL

1. Online transactions: Cloud SQL for MySQL 2. Customer feedback: BigQuery 3. Social media activity: Cloud Storage

You are storing data in Cloud Storage for a machine learning project. The data is frequently accessed during the model training phase, minimally accessed after 30 days, and unlikely to be accessed after 90 days. You need to choose the appropriate storage class for the different stages of the project to minimize cost. What should you do?

Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

Store the data in Nearline storage during the model training phase. Transition the data to Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.

Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

Store the data in Nearline storage during the model training phase. Transition the data to Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

Store the data in Standard storage during the model training phase. Transition the data to Durable Reduced Availability (DRA) storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage. The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis. The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

Use Dataproc to run the transformations on a cluster.

Use Dataflow with a custom template for the transformation logic.

Use Cloud Data Fusion to visually design and manage the pipeline.

Use Dataform to define the transformations in SQLX.

Use Dataproc to run the transformations on a cluster.

Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability. What should you do?

Create a dual-region bucket, and upload the files to this bucket.

Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.

Create a multi-region bucket, and upload the files to this bucket.

Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.

You are designing an application that will interact with several BigQuery datasets. You need to grant the application's service account permissions that allow it to query and update tables within the datasets, and list all datasets in a project within your application. You want to follow the principle of least privilege. Which pre-defined IAM role(s) should you apply to the service account?

roles/bigquery.jobUser and roles/bigquery.dataOwner

roles/bigquery.connectionUser and roles/bigquery.dataViewer

roles/bigquery.user and roles/bigquery.filteredDataViewer

Your company is setting up an enterprise business intelligence platform. You need to limit data access between many different teams while following the Google-recommended approach. What should you do first?

Create a Looker (Google Cloud core) instance, and configure different Looker groups for each team.

Create a separate Looker Studio report for each team, and share each report with the individuals within each team.

Create one Looker Studio report with multiple pages, and add each team's data as a separate data source to the report.

Create a Looker (Google Cloud core) instance, and create a separate dashboard for each team.

Create a Looker (Google Cloud core) instance, and configure different Looker groups for each team.

Your company is adopting BigQuery as their data warehouse platform. Your team has experienced Python developers. You need to recommend a fully-managed tool to build batch ETL processes that extract data from various source systems, transform the data using a variety of Google Cloud services, and load the transformed data into BigQuery. You want this tool to leverage your team's Python skills. What should you do?

Use Cloud Composer with pre-built operators.

Deploy Cloud Data Fusion and included plugins.

Use Cloud Composer with pre-built operators.

Use Dataflow and pre-built templates.

You need to create a data pipeline for a new application. Your application will stream data that needs to be enriched and cleaned. Eventually, the data will be used to train machine learning models. You need to determine the appropriate data manipulation methodology and which Google Cloud services to use in this pipeline. What should you choose?

ETL; Cloud Data Fusion -> Cloud Storage

ELT; Cloud SQL -> Analytics Hub

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?

Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.

Use Cloud Client Libraries to transfer the data over the internet.

Use the gcloud storage command to transfer the data over the internet.

Compress the data, upload it to multiple cloud storage providers, and then transfer the data to Cloud Storage.

Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.

Google Associate Data Practitioner Practice Test 2 - Free Online Exam Questions 41-80

Your organization needs to implement near real-time analytics for thousands of events arriving each second in Pub/Sub. The incoming messages require transformations. You need to configure a pipeline that processes, transforms, and loads the data into BigQuery while minimizing development time. What should you do?

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Google Associate Data Practitioner Practice Test 2

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