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

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Google Associate Data Practitioner Sample Questions (Q20-Q25):

NEW QUESTION # 20
You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users' personally identifiable information (PII) that will be analyzed with SQL.
3. A large volume of small document files that will power other applications.
You need to select the appropriate tool for each data type given the required use case, while following Google- recommended practices. Which should you choose?

A. Filestore Cloud SQL for PostgreSQL Datastore
B. Cloud Storage CloudSQL for PostgreSQL Bigtable
C. Filestore Bigtable BigQuery
D. Cloud Storage BigQuery Firestore

Answer: D

NEW QUESTION # 21
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 (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;
C. CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select 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: B

NEW QUESTION # 22
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. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
D. Create BigQuery scheduled queries to define the transformations in SQL.

Answer: B

NEW QUESTION # 23
Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?

A. Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.
B. Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.
C. Use authorized views to share query results with the payroll specialist.
D. Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.

Answer: C

NEW QUESTION # 24
You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

A. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
B. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
C. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
D. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.

Answer: C

NEW QUESTION # 25
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