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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data-Driven Decision Making	10-20%	- Define success metrics - Identify stakeholders and requirements - Assess data quality and completeness - Translate business requirements into data solutions
Topic 2: Data Processing and Analytics	20-30%	- Build and maintain data pipelines - Query and analyze datasets - Apply statistical methods for analysis - Use BigQuery and SQL for analytics - Aggregate and summarize data
Topic 3: 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
Topic 4: Data Preparation and Exploration	20-30%	- Ingest and acquire data - Transform and prepare data for analysis - Perform exploratory data analysis (EDA) - Identify data quality issues - Explore data through visualization and queries

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Google Associate Data Practitioner Sample Questions (Q12-Q17):

NEW QUESTION # 12
Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

A. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
B. Use Dataform to define the transformations in SQLX.
C. Create BigQuery scheduled queries to define the transformations in SQL.
D. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.

Answer: B

NEW QUESTION # 13
Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

A. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
B. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
C. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
D. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.

Answer: B

NEW QUESTION # 14
You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

A. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
B. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
C. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
D. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.

Answer: B

NEW QUESTION # 15
You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege. What should you do?

A. Request the Dataflow Developer role.
B. Request the Dataflow Viewer role.
C. Request the Dataflow Worker role.
D. Request the Dataflow Admin role.

Answer: A

NEW QUESTION # 16
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

A. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
B. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
C. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
D. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.

Answer: A

NEW QUESTION # 17
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