Google Associate-Data-Practitioner Actual Tests : Google Cloud Associate Data Practitioner

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

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 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.
• B. 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.
• C. 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.
• D. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.

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 Colab Enterprise to develop a custom model for purchase prediction.
• B. Use Vertex AI Workbench to develop a custom model for purchase prediction.
• C. Use BigQuery ML to create a logistic regression model for purchase prediction.
• D. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.

You are a database administrator managing sales transaction data by region stored in a BigQuery table. You need to ensure that each sales representative can only see the transactions in their region. What should you do?

• A. Grant the appropriate 1AM permissions on the dataset.
• B. Create a data masking rule.
• C. Add a policy tag in BigQuery.
• D. Create a row-level access policy.

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?

• A. Create a transfer job using the Google Cloud CLI, and specify the Standard storage class with the - custom-storage-class flag.
• B. Create an agent-based transfer job that utilizes multiple transfer agents on Compute Engine instances.
• C. Create a batch Dataflow job that is scheduled weekly to migrate the data from Amazon S3 to Cloud Storage.
• D. Create parallel transfer jobs using include and exclude prefixes.