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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation and Ingestion	30%	- Data ingestion into Google Cloud services 1. Cloud Storage ingestion patterns 2. Streaming ingestion with Pub/Sub concepts
Data Management	25%	- Storage and data organization 1. BigQuery datasets and tables management 2. Cloud Storage lifecycle policies - Data governance and security 1. Data lifecycle management 2. IAM and access control
Data Analysis and Presentation	27%	- Querying and analyzing data 1. Data aggregation and transformation 2. BigQuery SQL usage - Data visualization 1. Looker / Looker Studio reporting
Data Pipeline Orchestration	18%	- Pipeline design and automation 1. Workflow orchestration concepts (e.g., Cloud Composer) 2. Dataflow pipelines

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100% Pass Google - ADP - Associate Data Practitioner –Reliable Interactive Questions

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Google Associate Data Practitioner Sample Questions (Q42-Q47):

NEW QUESTION # 42
Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A. Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job I
B. Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
C. Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
D. Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.

Answer: D

NEW QUESTION # 43
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_uype='logisric_reg') AS SELECT * from cusromer_data;
B. CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select churned as label FROM customer_data;
C. CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ' except(churned) FROM customer data;
D. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;

Answer: D

NEW QUESTION # 44
Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

A. Enable Object Versioning.
B. Set a bucket retention policy.
C. Change the bucket storage class to Archive.
D. Set temporary object holds.

Answer: B

NEW QUESTION # 45
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 derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
B. Apply a filter to only show products with a positive profit margin.
C. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
D. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.

Answer: D

NEW QUESTION # 46
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 Dataflow with a custom template for the transformation logic.
B. Use Dataform to define the transformations in SQLX.
C. Use Dataproc to run the transformations on a cluster.
D. Use Cloud Data Fusion to visually design and manage the pipeline.

Answer: C

NEW QUESTION # 47
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