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NEW QUESTION # 19
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?
- A. 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 I
- B. Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
- C. Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
- D. Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
Answer: B
NEW QUESTION # 20
Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?
- A. Change the bucket storage class to Archive.
- B. Set a bucket retention policy.
- C. Set temporary object holds.
- D. Enable Object Versioning.
Answer: B
NEW QUESTION # 21
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 Cloud Composer to orchestrate the Spark job and email the report.
- B. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
- C. Use Cloud Run functions to trigger the Spark job and email the report.
- D. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
Answer: D
NEW QUESTION # 22
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?
- A. Use Cloud Data Fusion to visually design and manage the pipeline.
- B. Use Dataproc to run the transformations on a cluster.
- C. Use Dataflow with a custom template for the transformation logic.
- D. Use Dataform to define the transformations in SQLX.
Answer: B
NEW QUESTION # 23
You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?
- A. Grant each partner read access to the BigQuery dataset by using 1AM roles.
- B. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
- C. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.
- D. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
Answer: D
NEW QUESTION # 24
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?
- A. Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
- B. Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
- C. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
- D. Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
Answer: A
NEW QUESTION # 25
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?
- A. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
- B. 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.
- C. 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.
- D. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
Answer: D
NEW QUESTION # 26
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. 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.
- B. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
- C. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
- D. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
Answer: D
NEW QUESTION # 27
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. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
- B. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
- C. Migrate the Spark jobs to Dataproc on Compute Engine.
- D. Migrate the Spark jobs to Dataproc Serverless.
Answer: D
NEW QUESTION # 28
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 dat a. What should you do?
- A. 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.
- B. Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
- C. 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.
- D. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
Answer: A
NEW QUESTION # 29
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?
- A. Use a Transfer Appliance.
- B. Use Cloud Storage FUSE.
- C. Use gcloud storage commands.
- D. Use Storage Transfer Service.
Answer: D
NEW QUESTION # 30
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?
- A. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
- B. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
- C. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
- D. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
Answer: C
NEW QUESTION # 31
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?
- A. Dataproc Serverless Bigtable
- B. BigQuery Analytics Hub
- C. Dataflow BigQuery
- D. Cloud Composer Cloud SQL for MySQL
Answer: C
NEW QUESTION # 32
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?
- A. Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
- B. Create a Cloud Run function that runs daily and deletes files older than seven days.
- C. Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
- D. Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
Answer: D
NEW QUESTION # 33
Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?
- A. Use Dataform to define the transformations in SQLX.
- B. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
- C. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
- D. Create BigQuery scheduled queries to define the transformations in SQL.
Answer: A
NEW QUESTION # 34
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?
- A. 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.
- B. 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.
- C. 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.
- D. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
Answer: C
NEW QUESTION # 35
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 the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.
- B. Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
- C. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.
- D. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
Answer: B
NEW QUESTION # 36
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?
- A. 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.
- B. 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.
- C. 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.
- D. 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.
Answer: A
NEW QUESTION # 37
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?
- A. 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.
- B. 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.
- C. Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
- D. Create a custom quota for each analyst in BigQuery.
Answer: B
NEW QUESTION # 38
You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost- effective, fully-managed, serverless solution to build ELT processes with SQL pipelines.
Your solution must include source code control, environment parameterization, and data quality checks. What should you do?
- A. Use Cloud Data Fusion to visually design and manage the pipelines.
- B. Use Dataform to build, orchestrate, and monitor the pipelines.
- C. Use Dataproc to run MapReduce jobs for distributed data processing.
- D. Use Cloud Composer to orchestrate and run data workflows.
Answer: B
NEW QUESTION # 39
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 (PII) 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?
- A. Filestore Bigtable BigQuery
- B. Filestore Cloud SQL for PostgreSQL Datastore
- C. Cloud Storage BigQuery Firestore
- D. Cloud Storage CloudSQL for PostgreSQL Bigtable
Answer: C
NEW QUESTION # 40
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?
- A. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
- B. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
- C. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
- D. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
Answer: D
NEW QUESTION # 41
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?
- A. Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
- B. Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
- C. 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.
- D. Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
Answer: C
NEW QUESTION # 42
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?
- A. Use Dataflow pipelines.
- B. Use Cloud Data Fusion pipelines.
- C. Use Dataform workflows.
- D. Use Cloud Composer operators.
Answer: C
NEW QUESTION # 43
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