Google Associate-Data-Practitioner : Google Cloud Associate Data Practitioner

You want to build a model to predict the likelihood of a customer clicking on an online advertisement. You have historical data in BigQuery that includes features such as user demographics, ad placement, and previous click behavior. After training the model, you want to generate predictions on new data. Which model type should you use in BigQuery ML?

• A. Linear regression
• B. Matrix factorization
• C. K-means clustering
• D. Logistic regression

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. Export the dataset to a Cloud Storage bucket 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. Create authorized views in the team's Google Cloud project that is only accessible by the team.
• D. Enable domain restricted sharing on the project. Grant the team members the BigQuery Data Viewer IAM role on the dataset.

You are constructing a data pipeline to process sensitive customer data stored in a Cloud Storage bucket. You need to ensure that this data remains accessible, even in the event of a single-zone outage. What should you do?

• A. Store the data in Nearline storaqe.
• B. Store the data in a multi-region bucket.
• C. Enable Object Versioning on the bucket.
• D. Set up a Cloud CDN in front of the bucket.

You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

• A. Use Cloud Logging to create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.
• B. Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.
• C. Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline's metrics, such as CPU utilization and memory usage.
• D. Use Cloud Logging to identify error groups in the pipeline's logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.

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?

• A. Use Colab Enterprise to create a Jupyter notebook. Use the historical sales data to train a custom prediction model in Python.
• B. 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.
• C. 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.
• D. 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.