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Google Associate Data Practitioner Sample Questions:

1. You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?

A) Compare the confusion matrix.
B) Evaluate the data skewness.
C) Compare the two different models.
D) Evaluate data drift.

2. 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) Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
B) Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
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.

3. You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

A) Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
B) Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
C) Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
D) Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.

4. You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

A) Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
B) Use BigQuery ML to create a logistic regression model for purchase prediction.
C) Use Colab Enterprise to develop a custom model for purchase prediction.
D) Use Vertex Al Workbench to develop a custom model for purchase prediction.

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

Solutions: