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NEW QUESTION # 24
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?
- A. 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.
- B. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
- C. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
- D. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
Answer: A
NEW QUESTION # 25
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 Cloud Data Fusion to visually design and manage the pipelines.
- C. Use Dataform to build, orchestrate, and monitor the pipelines.
- D. Use Cloud Composer to orchestrate and run data workflows.
Answer: C
NEW QUESTION # 26
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. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
- B. Use Dataform to define the transformations in SQLX.
- 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: B
NEW QUESTION # 27
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. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery 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. 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.
- D. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
Answer: A
NEW QUESTION # 28
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. Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
- 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. 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
Answer: B
NEW QUESTION # 29
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. Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
- B. Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
- C. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
- D. Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
Answer: D
NEW QUESTION # 30
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?
- A. Create a multi-region bucket, and upload the files to this bucket.
- B. 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.
- C. 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.
- D. Create a dual-region bucket, and upload the files to this bucket.
Answer: D
NEW QUESTION # 31
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 Storage Transfer Service.
- B. Use Cloud Storage FUSE.
- C. Use a Transfer Appliance.
- D. Use gcloud storage commands.
Answer: A
NEW QUESTION # 32
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?
- A. Compare the confusion matrix.
- B. Compare the two different models.
- C. Evaluate data drift.
- D. Evaluate the data skewness.
Answer: C
NEW QUESTION # 33
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 Run functions to trigger 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 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 # 34
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. BigQuery Analytics Hub
- B. Dataflow BigQuery
- C. Dataproc Serverless Bigtable
- D. Cloud Composer Cloud SQL for MySQL
Answer: B
NEW QUESTION # 35
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. Apply a filter to only show products with a positive profit margin.
- C. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
- D. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
Answer: D
NEW QUESTION # 36
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?
- A. Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
- B. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
- C. Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
- D. 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.
Answer: A
NEW QUESTION # 37
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. Cloud Storage CloudSQL for PostgreSQL Bigtable
- B. Filestore Cloud SQL for PostgreSQL Datastore
- C. Cloud Storage BigQuery Firestore
- D. Filestore Bigtable BigQuery
Answer: C
NEW QUESTION # 38
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 Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
- B. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
- C. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow 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 # 39
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 Dataproc to run the transformations on a cluster.
- B. Use Dataform to define the transformations in SQLX.
- C. Use Dataflow with a custom template for the transformation logic.
- D. Use Cloud Data Fusion to visually design and manage the pipeline.
Answer: A
NEW QUESTION # 40
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?
- A. 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.
- B. 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.
- C. 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.
- D. 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.
Answer: B
NEW QUESTION # 41
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