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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q37-Q42):

NEW QUESTION # 37
You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

A. Use Cloud Composer to orchestrate the Spark job and email the report.
B. Use Cloud Run functions to trigger the Spark job and email the report.
C. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
D. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.

Answer: C

NEW QUESTION # 38
Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

A. CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select churned as label FROM customer_data;
B. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype='logisric_reg') AS SELECT * from cusromer_data;
C. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;
D. CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ' except(churned) FROM customer data;

Answer: C

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 BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.
C. Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
D. Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.

Answer: A

NEW QUESTION # 40
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. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
C. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
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 # 41
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 the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
B. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
C. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
D. 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.

Answer: C

NEW QUESTION # 42
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