Study with ADP Actual Test Questions

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. Filestore Cloud SQL for PostgreSQL Datastore
• B. Cloud Storage CloudSQL for PostgreSQL Bigtable
• C. Cloud Storage BigQuery Firestore
• D. Filestore Bigtable BigQuery

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 Vertex Al Workbench to develop a custom model for purchase prediction.
• B. Use BigQuery ML to create a logistic regression model for purchase prediction.
• C. Use Colab Enterprise to develop a custom 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 working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

• A. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
• B. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
• C. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
• D. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.

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

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.