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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q35-Q40):

NEW QUESTION # 35
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 Admin role.
B. Request the Dataflow Developer role.
C. Request the Dataflow Worker role.
D. Request the Dataflow Viewer role.

Answer: B

NEW QUESTION # 36
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 Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
B. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
C. 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.
D. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.

Answer: C

NEW QUESTION # 37
You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

A. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
B. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
C. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
D. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.

Answer: D

NEW QUESTION # 38
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 new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
B. Apply a filter to only show products with a positive profit margin.
C. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
D. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.

Answer: D

NEW QUESTION # 39
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 Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
B. Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
C. Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
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: A

NEW QUESTION # 40
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