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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q64-Q69):

NEW QUESTION # 64
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 a temporary table.
B. Create an object table.
C. Create an external table.
D. Create a native table.

Answer: B

NEW QUESTION # 65
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. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
C. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
D. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.

Answer: B

NEW QUESTION # 66
Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner's data in its own BigQuery dataset. Each partner should be able to access only their dat a. You want to share the data while following Google-recommended practices. What should you do?

A. Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub /Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.
B. Grant the partners the bigquery.user IAM role on the BigQuery project.
C. Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
D. Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.

Answer: D

NEW QUESTION # 67
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 # 68
Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

A. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
B. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
C. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
D. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.

Answer: A

NEW QUESTION # 69
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