ADP Real Exam Questions & Latest ADP Exam Papers

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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q55-Q60):

NEW QUESTION # 55
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 Cloud Data Fusion to visually design and manage the pipeline.
B. Use Dataform to define the transformations in SQLX.
C. Use Dataproc to run the transformations on a cluster.
D. Use Dataflow with a custom template for the transformation logic.

Answer: C

NEW QUESTION # 56
Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

A. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
B. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
C. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
D. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.

Answer: D

NEW QUESTION # 57
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 ' except(churned) 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 churned as label FROM customer_data;

Answer: C

NEW QUESTION # 58
You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

A. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
B. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
C. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
D. Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.

Answer: D

NEW QUESTION # 59
Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

A. Create an object table.
B. Create an external table.
C. Create a native table.
D. Create a temporary table.

Answer: A

NEW QUESTION # 60
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