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Google Associate Data Practitioner Sample Questions:

1. Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

A) Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
B) Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
C) Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
D) Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.

2. Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

A) Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
B) Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
C) Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
D) Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.

3. 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) Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
C) Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
D) Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.

4. You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

A) Use Cloud Client Libraries to transfer the data over the internet.
B) Use the gcloud storage command to transfer the data over the internet.
C) Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
D) Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.

5. You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

A) Use Cloud Logging to create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.
B) Use Cloud Logging to identify error groups in the pipeline's logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.
C) Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.
D) Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline's metrics, such as CPU utilization and memory usage.

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