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NEW QUESTION # 33
Your organization needs to store historical customer order dat
a. 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?
- A. Store the data in Cloud Storage using Standard storage.
- B. Store the data in Cloud Storage using Nearline storage.
- C. Store the data in Cloud Storage using Coldline storage.
- D. Store the data in Cloud Storage using Archive storage.
Answer: B
NEW QUESTION # 34
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?
- A. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
- B. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
- C. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
- D. Apply a filter to only show products with a positive profit margin.
Answer: B
NEW QUESTION # 35
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. Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
- C. Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the 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: C
NEW QUESTION # 36
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 Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
- C. 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.
- D. Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
Answer: B
NEW QUESTION # 37
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 Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
- B. Use Cloud Run functions to trigger the Spark job and email the report.
- C. Use Cloud Composer to orchestrate 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 # 38
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 the PythonOperator in Cloud Composer to clean the data and load it 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 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.
Answer: C
NEW QUESTION # 39
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. Set temporary object holds.
- B. Change the bucket storage class to Archive.
- C. Enable Object Versioning.
- D. Set a bucket retention policy.
Answer: D
NEW QUESTION # 40
Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?
- A. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
- B. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
- C. Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
- D. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
Answer: C
NEW QUESTION # 41
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. Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
- B. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
- C. Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
- D. Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
Answer: C
NEW QUESTION # 42
Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?
- A. Create a native table.
- B. Create a temporary table.
- C. Create an external table.
- D. Create an object table.
Answer: D
NEW QUESTION # 43
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?
- A. 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
- B. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
- C. 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.
- D. 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.
Answer: B
NEW QUESTION # 44
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?
- A. Use BigQuery ML to create a logistic regression model for purchase prediction.
- B. Use Colab Enterprise to develop a custom model for purchase prediction.
- C. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
- D. Use Vertex Al Workbench to develop a custom model for purchase prediction.
Answer: A
NEW QUESTION # 45
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 pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
- D. 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.
Answer: D
NEW QUESTION # 46
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?
- A. Request the Dataflow Admin role.
- B. Request the Dataflow Worker role.
- C. Request the Dataflow Viewer role.
- D. Request the Dataflow Developer role.
Answer: D
NEW QUESTION # 47
You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?
- A. Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.
- B. Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.
- C. Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.
- D. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
Answer: C
NEW QUESTION # 48
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. 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.
- B. Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
- C. Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
- D. Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
Answer: A
NEW QUESTION # 49
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 dat a. You want to share the data while following Google-recommended practices. What should you do?
- A. Grant the partners the bigquery.user IAM role on the BigQuery project.
- B. 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.
- C. 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.
- D. Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
Answer: B
NEW QUESTION # 50
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?
- A. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
- B. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
- C. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
- D. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
Answer: B
NEW QUESTION # 51
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?
- A. Use the gcloud storage command to transfer the data over the internet.
- B. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
- C. Use Cloud Client Libraries to transfer the data over the internet.
- D. Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.
Answer: B
NEW QUESTION # 52
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 Dataform to define the transformations in SQLX.
- B. Use Cloud Data Fusion to visually design and manage the pipeline.
- C. Use Dataproc to run the transformations on a cluster.
- D. Use Dataflow with a custom template for the transformation logic.
Answer: C
NEW QUESTION # 53
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. 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.
- B. 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.
- C. 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.
- D. 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.
Answer: B
NEW QUESTION # 54
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 # 55
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 Dataproc to run MapReduce jobs for distributed data processing.
- B. Use Dataform to build, orchestrate, and monitor the pipelines.
- C. Use Cloud Composer to orchestrate and run data workflows.
- D. Use Cloud Data Fusion to visually design and manage the pipelines.
Answer: B
NEW QUESTION # 56
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?
- A. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
- B. Use Cloud Scheduler to schedule the jobs to run.
- C. 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.
- D. Use Cloud Tasks to schedule and run the jobs asynchronously.
Answer: A
NEW QUESTION # 57
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 for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.
- B. Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
- C. Create a custom quota for each analyst in BigQuery.
- D. 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.
Answer: A
NEW QUESTION # 58
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