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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q58-Q63):

NEW QUESTION # 58
Your organization's website uses an on-premises MySQL as a backend database. You need to migrate the on-premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?

A. Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
B. Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.
C. Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.
D. Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.

Answer: A

NEW QUESTION # 59
Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one- time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

A. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
B. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
C. Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
D. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.

Answer: A

NEW QUESTION # 60
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 # 61
Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

A. Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
B. Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
C. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
D. Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.

Answer: D

NEW QUESTION # 62
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 Dataproc workflow templates to define and schedule the Spark job, and to email the report.
C. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
D. Use Cloud Run functions to trigger the Spark job and email the report.

Answer: B

NEW QUESTION # 63
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