ADP Exam Certification - New ADP Test Syllabus

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Google ADP Exam Syllabus Topics:

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

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New ADP Test Syllabus & ADP Exam Objectives Pdf

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Google Associate Data Practitioner Sample Questions (Q39-Q44):

NEW QUESTION # 39
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 Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
B. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
C. Use Cloud Run functions to transform and load the data into BigQuery.
D. Use Dataflow to transform the data and write the transformed data to BigQuery.

Answer: D

NEW QUESTION # 40
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. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
C. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
D. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.

Answer: A

NEW QUESTION # 41
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?

A. Apply a filter to only show products with a positive profit margin.
B. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
C. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
D. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

Answer: B

NEW QUESTION # 42
You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re- run risk reports when new data becomes available. What should you do?

A. Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.
B. Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
C. Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.
D. Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.

Answer: B

NEW QUESTION # 43
You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

A. Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
B. Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
C. Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
D. Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.

Answer: B

NEW QUESTION # 44
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