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Google ADP Exam Syllabus Topics:

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

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Most Probable Real Google Exam Questions in Google ADP PDF Format

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Google Associate Data Practitioner Sample Questions (Q54-Q59):

NEW QUESTION # 54
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 Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
C. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
D. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.

Answer: D

NEW QUESTION # 55
You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage.
The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

A. EL
B. ELT
C. ETL
D. ETLT

Answer: C

NEW QUESTION # 56
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 Coldline storage.
B. Store the data in Cloud Storage using Standard storage.
C. Store the data in Cloud Storage using Nearline storage.
D. Store the data in Cloud Storage using Archive storage.

Answer: C

NEW QUESTION # 57
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

A. Use Vertex Al Workbench to develop a custom model for purchase prediction.
B. Use Colab Enterprise to develop a custom model for purchase prediction.
C. Use BigQuery ML to create a logistic regression model for purchase prediction.
D. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.

Answer: C

NEW QUESTION # 58
You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

A. Use Dataform to define the transformations in SQLX.
B. Use Dataflow with a custom template for the transformation logic.
C. Use Cloud Data Fusion to visually design and manage the pipeline.
D. Use Dataproc to run the transformations on a cluster.

Answer: D

NEW QUESTION # 59
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