Google ADP : Associate Data Practitioner

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Google Associate Data Practitioner Sample Questions:

1. Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?

A) Create a multi-region bucket, and upload the files to this bucket.
B) Create a dual-region bucket, and upload the files to this bucket.
C) Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.
D) Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.

2. You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege. What should you do?

A) Request the Dataflow Developer role.
B) Request the Dataflow Viewer role.
C) Request the Dataflow Worker role.
D) Request the Dataflow Admin role.

3. Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one- time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

A) Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
B) Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
C) Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
D) Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.

4. Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

A) Use Cloud Composer operators.
B) Use Dataflow pipelines.
C) Use Dataform workflows.
D) Use Cloud Data Fusion pipelines.

5. 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) 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) Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.

Solutions: