Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.

Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.

Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.

Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.

Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.

You manage an ecommerce website that has a diverse range of products. You need to forecast future product demand accurately to ensure that your company has sufficient inventory to meet customer needs and avoid stockouts. Your company's historical sales data is stored in a BigQuery table. You need to create a scalable solution that takes into account the seasonality and historical data to predict product demand. What should you do?

Use the historical sales data to train and create a BigQuery ML time series model. Use the ML.FORECAST function call to output the predictions into a new BigQuery table.

Use Colab Enterprise to create a Jupyter notebook. Use the historical sales data to train a custom prediction model in Python.

Use the historical sales data to train and create a BigQuery ML linear regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table.

Use the historical sales data to train and create a BigQuery ML logistic regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table.

You are using your own data to demonstrate the capabilities of BigQuery to your organization's leadership team. You need to perform a one-time load of the files stored on your local machine into BigQuery using as little effort as possible. What should you do?

Execute the bq load command on your local machine.

Write and execute a Python script using the BigQuery Storage Write API library.

Create a Dataproc cluster, copy the files to Cloud Storage, and write an Apache Spark job using the spark-bigquery-connector.

Execute the bq load command on your local machine.

Create a Dataflow job using the Apache Beam FileIO and BigQueryIO connectors with a local runner.

Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?

Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.

Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.

Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.

Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.

Create a BigQuery materialized view that performs a cross join across all of the regional sales tables. Use the row_number() window function to query the new materialized view.

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?

Change the bucket storage class to Archive.

Your organization consists of two hundred employees on five different teams. The leadership team is concerned that any employee can move or delete all Looker dashboards saved in the Shared folder. You need to create an easy-to-manage solution that allows the five different teams in your organization to view content in the Shared folder, but only be able to move or delete their team-specific dashboard. What should you do?

1. Change the access level of the Shared folder to View for the All Users group. 2. Create Looker groups representing each of the five different teams, and add users to their corresponding group. 3. Create five subfolders inside the Shared folder. Grant each group the Manage Access, Edit access level to their corresponding subfolder.

1. Create Looker groups representing each of the five different teams, and add users to their corresponding group. 2. Create five subfolders inside the Shared folder. Grant each group the View access level to their corresponding subfolder.

1. Move all team-specific content into the dashboard owner s personal folder. 2. Change the access level of the Shared folder to View for the All Users group. 3. Instruct each user to create content for their team in the user's personal folder.

1. Change the access level of the Shared folder to View for the All Users group. 2. Create Looker groups representing each of the five different teams, and add users to their corresponding group. 3. Create five subfolders inside the Shared folder. Grant each group the Manage Access, Edit access level to their corresponding subfolder.

1. Change the access level of the Shared folder to View for the All Users group. 2. Create five subfolders inside the Shared folder. Grant each team member the Manage Access, Edit access level to their corresponding subfolder.

Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.

Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.

Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.

Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.

Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.

Your organization's website uses an on-premises MySQL as a backend database. You need to migrate the on-premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?

Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.

Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.

Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.

Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.

Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.

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?

Define a new measure that calculates the profit margin by using the existing revenue and cost fields.

Apply a filter to only show products with a positive profit margin.

Define a new measure that calculates the profit margin by using the existing revenue and cost fields.

Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.

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?

Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.

Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the --daisy-chain option.

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

Use the gcloud storage cp command to copy all files from on-premises to Cloud Storage using the --no-clobber option.

You work for a gaming company that collects real-time player activity data. This data is streamed into Pub/Sub and needs to be processed and loaded into BigQuery for analysis. The processing involves filtering, enriching, and aggregating the data before loading it into partitioned BigQuery tables. You need to design a pipeline that ensures low latency and high throughput while following a Google-recommended approach. What should you do?

Use Dataflow to create a streaming pipeline that reads the data from Pub/Sub, processes the data, and writes it to BigQuery using the streaming API.

Use Cloud Composer to orchestrate a workflow that reads the data from Pub/Sub, processes the data using a Python script, and writes it to BigQuery.

Use Dataproc to create an Apache Spark streaming job that reads the data from Pub/Sub, processes the data, and writes it to BigQuery.

Use Dataflow to create a streaming pipeline that reads the data from Pub/Sub, processes the data, and writes it to BigQuery using the streaming API.

Use Cloud Run functions to subscribe to the Pub/Sub topic, process the data, and write it to BigQuery using the streaming API.

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?

CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;

CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype='logisric_reg') AS SELECT * from cusromer_data;

CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;

CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select churned as label FROM customer_data;

CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ' except(churned) FROM customer data;

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?

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.

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.

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.

Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.

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?

Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.

Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.

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.

Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.

Your team uses Google Sheets to track budget data that is updated daily. The team wants to compare budget data against actual cost data, which is stored in a BigQuery table. You need to create a solution that calculates the difference between each day's budget and actual costs. You want to ensure that your team has access to daily-updated results in Google Sheets. What should you do?

Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table, and open it by using Connected Sheets.

Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table as a CSV file and open the file in Google Sheets.

Download the budget data as a CSV file and upload the CSV file to a Cloud Storage bucket. Create a new BigQuery table from Cloud Storage, and join the actual cost table with it. Open the joined BigQuery table by using Connected Sheets.

Download the budget data as a CSV file, and upload the CSV file to create a new BigQuery table. Join the actual cost table with the new BigQuery table, and save the results as a CSV file. Open the CSV file in Google Sheets.

Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table, and open it by using Connected Sheets.

You have a Cloud SQL for PostgreSQL database that stores sensitive historical financial data. You need to ensure that the data is uncorrupted and recoverable in the event that the primary region is destroyed. The data is valuable, so you need to prioritize recovery point objective (RPO) over recovery time objective (RTO). You want to recommend a solution that minimizes latency for primary read and write operations. What should you do?

Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with synchronous replication to a secondary instance in a different zone.

Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with asynchronous replication to a secondary instance in a different region.

Configure the Cloud SQL for PostgreSQL instance for multi-region backup locations.

Configure the Cloud SQL for PostgreSQL instance for regional availability (HA). Back up the Cloud SQL for PostgreSQL database hourly to a Cloud Storage bucket in a different region.

Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with synchronous replication to a secondary instance in a different zone.

You are building a batch data pipeline to process 100 GB of structured data from multiple sources for daily reporting. You need to transform and standardize the data prior to loading the data to ensure that it is stored in a single dataset. You want to use a low-code solution that can be easily built and managed. What should you do?

Use Cloud Data Fusion to ingest the data, perform data cleaning and transformation, and load the data into BigQuery.

Use Cloud Data Fusion to ingest data and load the data into BigQuery. Use Looker Studio to perform data cleaning and transformation.

Use Cloud Data Fusion to ingest the data, perform data cleaning and transformation, and load the data into BigQuery.

Use Cloud Data Fusion to ingest the data, perform data cleaning and transformation, and load the data into Cloud SQL for PostgreSQL.

Use Cloud Storage to store the data. Use Cloud Run functions to perform data cleaning and transformation, and load the data into BigQuery.

You are working on a project that requires analyzing daily social media data. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?

Use Dataflow to transform the data and write the transformed data to BigQuery.

Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.

Use Cloud Run functions to transform and load the data into BigQuery.

Use Dataflow to transform the data and write the transformed data to BigQuery.

Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.

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?

Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.

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.

Grant each partner read access to the BigQuery dataset by using 1AM roles.

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.

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?

Use Dataform to define the transformations in SQLX.

Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.

Create BigQuery scheduled queries to define the transformations in SQL.

Use Dataform to define the transformations in SQLX.

Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.

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?

Create a new Cloud Composer environment and copy DAGs to the Cloud Composer dags/ folder.

Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.

Create a new Cloud Composer environment and copy DAGs to the Cloud Composer dags/ folder.

Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.

Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.

You have an existing weekly Storage Transfer Service transfer job from Amazon S3 to a Nearline Cloud Storage bucket in Google Cloud. Each week, the job moves a large number of relatively small files. As the number of files to be transferred each week has grown over time, you are at risk of no longer completing the transfer in the allocated time frame. You need to decrease the total transfer time by replacing the process. Your solution should minimize costs where possible. What should you do?

Create parallel transfer jobs using include and exclude prefixes.

Create a transfer job using the Google Cloud CLI, and specify the Standard storage class with the ---custom-storage-class flag.

Create parallel transfer jobs using include and exclude prefixes.

Create a batch Dataflow job that is scheduled weekly to migrate the data from Amazon S3 to Cloud Storage.

Create an agent-based transfer job that utilizes multiple transfer agents on Compute Engine instances.

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?

Use Dataform to build, orchestrate, and monitor the pipelines.

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

Use Dataform to build, orchestrate, and monitor the pipelines.

Use Dataproc to run MapReduce jobs for distributed data processing.

Use Cloud Composer to orchestrate and run data workflows.

You have a Cloud SQL for PostgreSQL database that stores sensitive historical financial data. You need to ensure that the data is uncorrupted and recoverable in the event that the primary region is destroyed. The data is valuable, so you need to prioritize recovery point objective (RPO) over recovery time objective (RTO). You want to recommend a solution that minimizes latency for primary read and write operations. What should you do?

Configure the Cloud SQL for PostgreSQL instance for multi-region backup locations.

Configure the Cloud SQL for PostgreSQL instance for regional availability (HA). Back up the Cloud SQL for PostgreSQL database hourly to a Cloud Storage bucket in a different region.

Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with synchronous replication to a secondary instance in a different zone.

Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with asynchronous replication to a secondary instance in a different region.

You work for a retail company that collects customer data from various sources:Online transactions: Stored in a MySQL databaseCustomer feedback: Stored as text files on a company serverSocial media activity: Streamed in real-time from social media platformsYou need to design a data pipeline to extract and load the data into the appropriate Google Cloud storage system(s) for further analysis and ML model training. What should you do?

Extract and load the online transactions data into BigQuery. Load the customer feedback data into Cloud Storage. Stream the social media activity by using Pub/Sub and Dataflow, and store the data in BigQuery.

Copy the online transactions data into Cloud SQL for MySQL. Import the customer feedback into BigQuery. Stream the social media activity into Cloud Storage.

Extract and load the online transactions data into BigQuery. Load the customer feedback data into Cloud Storage. Stream the social media activity by using Pub/Sub and Dataflow, and store the data in BigQuery.

Extract and load the online transactions data, customer feedback data, and social media activity into Cloud Storage.

Extract and load the online transactions data into Bigtable. Import the customer feedback data into Cloud Storage. Store the social media activity in Cloud SQL for MySQL.

Google Associate Data Practitioner Practice Test 3 - Free Online Exam Prep & Questions

You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

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Google Associate Data Practitioner Practice Test 3

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