Exam Questions for Google ADP in PDF Format

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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Processing and Analytics	20-30%	- Build and maintain data pipelines - Aggregate and summarize data - Query and analyze datasets - Apply statistical methods for analysis - Use BigQuery and SQL for analytics
Topic 2: Data-Driven Decision Making	10-20%	- Identify stakeholders and requirements - Assess data quality and completeness - Translate business requirements into data solutions - Define success metrics
Topic 3: Data Visualization and Insights	20-30%	- Interpret and communicate findings - Choose appropriate visualization types - Create dashboards and reports - Present data insights to stakeholders - Build visualizations using Looker Studio
Topic 4: Data Preparation and Exploration	20-30%	- Perform exploratory data analysis (EDA) - Ingest and acquire data - Explore data through visualization and queries - Transform and prepare data for analysis - Identify data quality issues

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Google Associate Data Practitioner Sample Questions (Q25-Q30):

NEW QUESTION # 25
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 Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
B. Use the PythonOperator in Cloud Composer 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 # 26
You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.
Which Google Cloud services should you recommend for the pipeline?

A. Dataproc Serverless Bigtable
B. BigQuery Analytics Hub
C. Cloud Composer Cloud SQL for MySQL
D. Dataflow BigQuery

Answer: D

NEW QUESTION # 27
Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

A. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
B. Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
C. Create a view that filters out rows that are older than one year.
D. Require users to specify a partition filter using the alter table statement in SQL.

Answer: A

NEW QUESTION # 28
You have a BigQuery dataset containing sales dat
a. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

A. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
B. Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.
C. Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.
D. Store all data in a single BigQuery table without partitioning or lifecycle policies.

Answer: B

NEW QUESTION # 29
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 ' except(churned) FROM customer 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 churned as label FROM customer_data;

Answer: C

NEW QUESTION # 30
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