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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data-Driven Decision Making	10-20%	- Define success metrics - Assess data quality and completeness - Translate business requirements into data solutions - Identify stakeholders and requirements
Data Preparation and Exploration	20-30%	- Explore data through visualization and queries - Transform and prepare data for analysis - Ingest and acquire data - Perform exploratory data analysis (EDA) - Identify data quality issues
Data Processing and Analytics	20-30%	- Build and maintain data pipelines - Use BigQuery and SQL for analytics - Query and analyze datasets - Aggregate and summarize data - Apply statistical methods for analysis
Data Visualization and Insights	20-30%	- Present data insights to stakeholders - Choose appropriate visualization types - Interpret and communicate findings - Build visualizations using Looker Studio - Create dashboards and reports

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Google Associate Data Practitioner Sample Questions (Q11-Q16):

NEW QUESTION # 11
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. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
B. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
C. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
D. 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.

Answer: D

NEW QUESTION # 12
Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?

A. Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.
B. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
C. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
D. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.

Answer: B

NEW QUESTION # 13
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 Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
B. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run 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.

Answer: C

NEW QUESTION # 14
Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?

A. Use authorized views to share query results with the payroll specialist.
B. Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
C. Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.
D. Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.

Answer: A

NEW QUESTION # 15
Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?

A. Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
B. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
C. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
D. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.

Answer: A

NEW QUESTION # 16
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