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Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

A
B
C
D

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?

A Create authorized views in the team’s Google Cloud project that is only accessible by the team.
B Create a private exchange using Analytics Hub with data egress restriction, and grant access to the team members.
C Enable domain restricted sharing on the project. Grant the team members the BigQuery Data Viewer IAM role on the dataset.
D 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?

A Create a single-region bucket with Autoclass enabled.
B Create a single-region bucket. Configure a Cloud Scheduler job that runs every 24 hours and changes the storage class based on upload date.
C Create a single-region bucket with custom Object Lifecycle Management policies based on upload date.
D 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?

A Request the Dataflow Developer role.
B Request the Dataflow Viewer role.
C Request the Dataflow Worker role.
D 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?

A
1. Dataproc Serverless
2. Bigtable
B
1. Cloud Composer
2. Cloud SQL for MySQL
C
1. BigQuery
2. Analytics Hub
D
1. Dataflow
2. BigQuery

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

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

A 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.
B 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.
C Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
D 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 need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage. The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

A EL
B ELT
C ETL
D ETLT

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?

A Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
B Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
C 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.
D 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?

A Use Google-managed encryption keys (GMEK).
B Use customer-managed encryption keys (CMEK).
C Use customer-supplied encryption keys (CSEK).
D Use customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.

Your company has several retail locations. Your company tracks the total number of sales made at each location each day. You want to use SQL to calculate the weekly moving average of sales by location to identify trends for each store. Which query should you use?

A
B
C
D

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?

A Create a partition by transaction date, and set the partition expiration policy to seven years.
B Set the table-level retention policy in BigQuery to seven years.
C Set the dataset-level retention policy in BigQuery to seven years.
D Export the BigQuery tables to Cloud Storage daily, and enforce a lifecycle management policy that has a seven-year retention rule.

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.

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 Serverless.
B Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
C Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
D Migrate the Spark jobs to Dataproc on Compute Engine.