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Google Associate Data Practitioner Sample Questions:

1. You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users' personally identifiable information (PII) that will be analyzed with SQL.
3. A large volume of small document files that will power other applications.
You need to select the appropriate tool for each data type given the required use case, while following Google- recommended practices. Which should you choose?

A) Filestore Cloud SQL for PostgreSQL Datastore
B) Cloud Storage BigQuery Firestore
C) Cloud Storage CloudSQL for PostgreSQL Bigtable
D) Filestore Bigtable BigQuery

2. 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) 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.
B) 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.
C) Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
D) Grant the partners the bigquery.user IAM role on the BigQuery project.

3. 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 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.
B) 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.
C) 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.
D) Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.

4. 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 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.
B) Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
C) Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
D) Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.

5. You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A) Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
B) Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
C) Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
D) Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.

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