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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q44-Q49):

NEW QUESTION # 44
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. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
B. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
C. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
D. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.

Answer: C

NEW QUESTION # 45
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 Dataproc workflow templates to define and schedule the Spark job, and to email the report.
B. Use Cloud Composer to orchestrate the Spark job and 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: A

NEW QUESTION # 46
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. 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.
B. Store all data in a single BigQuery table without partitioning or lifecycle policies.
C. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
D. 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.

Answer: A

NEW QUESTION # 47
Your organization has several datasets in their data warehouse in BigQuery. Several analyst teams in different departments use the datasets to run queries. Your organization is concerned about the variability of their monthly BigQuery costs. You need to identify a solution that creates a fixed budget for costs associated with the queries run by each department. What should you do?

A. Assign each analyst to a separate project associated with their department. Create a single reservation for each department by using BigQuery editions. Create assignments for each project in the appropriate reservation.
B. Assign each analyst to a separate project associated with their department. Create a single reservation by using BigQuery editions. Assign all projects to the reservation.
C. Create a custom quota for each analyst in BigQuery.
D. Create a single reservation by using BigQuery editions. Assign all analysts to the reservation.

Answer: A

NEW QUESTION # 48
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. 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.
B. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
C. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
D. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.

Answer: A

NEW QUESTION # 49
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