Google ADP : Associate Data Practitioner

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Google ADP Exam Syllabus Topics:

Google Associate Data Practitioner Sample Questions:

1. Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

A) CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ' except(churned) FROM customer data;
B) CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype='logisric_reg') AS SELECT * from cusromer_data;
C) CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select churned as label FROM customer_data;
D) CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;

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

3. You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?

A) Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
B) Use Cloud Run functions to transform and load the data into BigQuery.
C) Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
D) Use Dataflow to transform the data and write the transformed data to BigQuery.

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

5. Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

A) Use Cloud Scheduler to schedule the jobs to run.
B) Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
C) Use Cloud Tasks to schedule and run the jobs asynchronously.
D) Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.

Solutions: