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Google ADP Exam Syllabus Topics:

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

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

NEW QUESTION # 39
Your organization plans to move their on-premises environment to Google Cloud. Your organization's network bandwidth is less than 1 Gbps. You need to move over 500 ## of data to Cloud Storage securely, and only have a few days to move the dat a. What should you do?

A. Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
B. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
C. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
D. Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.

Answer: D

NEW QUESTION # 40
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 Data Fusion and Wrangler flow. Schedule the flow to run 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 Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.

Answer: C

NEW QUESTION # 41
Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

A. Set temporary object holds.
B. Change the bucket storage class to Archive.
C. Set a bucket retention policy.
D. Enable Object Versioning.

Answer: C

NEW QUESTION # 42
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. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
B. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
C. Apply a filter to only show products with a positive profit margin.
D. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.

Answer: D

NEW QUESTION # 43
You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

A. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
B. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
C. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
D. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.

Answer: C

NEW QUESTION # 44
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