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Google Associate Data Practitioner Sample Questions (Q50-Q55):
NEW QUESTION # 50
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 processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task 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 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.
- D. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
Answer: A
NEW QUESTION # 51
You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?
- A. Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
- B. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
- C. Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.
- D. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
Answer: D
NEW QUESTION # 52
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?
- A. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
- B. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
- C. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
- D. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
Answer: B
NEW QUESTION # 53
You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?
- A. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
- B. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
- C. Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
- D. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
Answer: C
NEW QUESTION # 54
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?
- A. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
- B. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
- C. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
- D. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
Answer: B
NEW QUESTION # 55
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