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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q44-Q49):

NEW QUESTION # 44
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 Cloud Data Fusion to visually design and manage the pipeline.
B. Use Dataflow with a custom template for the transformation logic.
C. Use Dataform to define the transformations in SQLX.
D. Use Dataproc to run the transformations on a cluster.

Answer: D

NEW QUESTION # 45
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. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
B. Use Vertex Al Workbench to develop a custom model for purchase prediction.
C. Use BigQuery ML to create a logistic regression model for purchase prediction.
D. Use Colab Enterprise to develop a custom model for purchase prediction.

Answer: C

NEW QUESTION # 46
You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types.
You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?

A. Configure lifecycle management rules to transition objects to appropriate storage classes based on access patterns. Set up Object Versioning for all objects to meet immutability requirements.
B. Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.
C. Move objects to different storage classes based on their age and access patterns. Use Cloud Key Management Service (Cloud KMS) to encrypt specific objects with customer-managed encryption keys (CMEK) to meet immutability requirements.
D. Create a Cloud Run function to periodically check object metadata, and move objects to the appropriate storage class based on age and access patterns. Use object holds to enforce immutability for specific objects.

Answer: B

NEW QUESTION # 47
Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

A. Use Dataform workflows.
B. Use Cloud Composer operators.
C. Use Dataflow pipelines.
D. Use Cloud Data Fusion pipelines.

Answer: A

NEW QUESTION # 48
You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

A. Update dataset privileges by using the SQL GRANT statement.
B. Enable access control by using IAM roles.
C. Export the data to Cloud Storage, and use signed URLs to authorize access.
D. Encrypt the data by using customer-managed encryption keys (CMEK).

Answer: B

NEW QUESTION # 49
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