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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Processing and Analytics	20-30%	- Query and analyze datasets - Aggregate and summarize data - Use BigQuery and SQL for analytics - Apply statistical methods for analysis - Build and maintain data pipelines
Topic 2: Data Preparation and Exploration	20-30%	- Ingest and acquire data - Perform exploratory data analysis (EDA) - Explore data through visualization and queries - Transform and prepare data for analysis - Identify data quality issues
Topic 3: Data-Driven Decision Making	10-20%	- Translate business requirements into data solutions - Assess data quality and completeness - Identify stakeholders and requirements - Define success metrics
Topic 4: Data Visualization and Insights	20-30%	- Interpret and communicate findings - Build visualizations using Looker Studio - Present data insights to stakeholders - Create dashboards and reports - Choose appropriate visualization types

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Google Associate Data Practitioner Sample Questions (Q33-Q38):

NEW QUESTION # 33
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 # 34
Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department. What should you do?

A. Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.
B. Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.
C. Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.
D. Create a custom quota for each analyst in BigQuery.

Answer: C

NEW QUESTION # 35
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. Use Cloud Run functions to transform and load the data into BigQuery.
B. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
C. Use Dataflow to transform the data and write the transformed data to BigQuery.
D. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.

Answer: C

NEW QUESTION # 36
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 processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
C. 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.
D. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.

Answer: B

NEW QUESTION # 37
Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

A. Store the data in Cloud Storage using Nearline storage.
B. Store the data in Cloud Storage using Archive storage.
C. Store the data in Cloud Storage using Standard storage.
D. Store the data in Cloud Storage using Coldline storage.

Answer: A

NEW QUESTION # 38
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