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Google Associate Data Practitioner Sample Questions:

1. 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) Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
B) Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
C) Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
D) Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.

2. 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?

A) Use Cloud Data Fusion to visually design and manage the pipelines.
B) Use Cloud Composer to orchestrate and run data workflows.
C) Use Dataform to build, orchestrate, and monitor the pipelines.
D) Use Dataproc to run MapReduce jobs for distributed data processing.

3. You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re- run risk reports when new data becomes available. What should you do?

A) Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.
B) Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
C) Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.
D) Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.

4. 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?

A) Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
B) Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
C) Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
D) Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.

5. You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

A) Create a Cloud Run function that runs daily and deletes files older than seven days.
B) Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
C) Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
D) Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.

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