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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner's data in its own BigQuery dataset. Each partner should be able to access only their dat a. You want to share the data while following Google-recommended practices. What should you do?

A) Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
B) Grant the partners the bigquery.user IAM role on the BigQuery project.
C) Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub /Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.
D) Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.

2. You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

A) Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
B) Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
C) Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
D) Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.

3. You are storing data in Cloud Storage for a machine learning project. The data is frequently accessed during the model training phase, minimally accessed after 30 days, and unlikely to be accessed after 90 days. You need to choose the appropriate storage class for the different stages of the project to minimize cost. What should you do?

A) Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
B) Store the data in Nearline storage during the model training phase. Transition the data to Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
C) Store the data in Standard storage during the model training phase. Transition the data to Durable Reduced Availability (DRA) storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
D) Store the data in Nearline storage during the model training phase. Transition the data to Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.

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

5. Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

A) Create a native table.
B) Create an external table.
C) Create an object table.
D) Create a temporary table.

Solutions: