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

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Google Associate Data Practitioner Sample Questions (Q22-Q27):

NEW QUESTION # 22
You have a Dataflow pipeline that processes website traffic logs stored in Cloud Storage and writes the processed data to BigQuery. You noticed that the pipeline is failing intermittently. You need to troubleshoot the issue. What should you do?

A. Use Cloud Logging to identify error groups in the pipeline's logs. Use Cloud Monitoring to create a dashboard that tracks the number of errors in each group.
B. Use Cloud Logging to view error messages in the pipeline's logs. Use Cloud Monitoring to analyze the pipeline's metrics, such as CPU utilization and memory usage.
C. Use Cloud Logging to create a chart displaying the pipeline's error logs. Use Metrics Explorer to validate the findings from the chart.
D. Use the Dataflow job monitoring interface to check the pipeline's status every hour. Use Cloud Profiler to analyze the pipeline's metrics, such as CPU utilization and memory usage.

Answer: B

NEW QUESTION # 23
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 Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
B. 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.
C. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
D. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.

Answer: B

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

Answer: B

NEW QUESTION # 25
You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
B. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
C. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
D. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.

Answer: C

NEW QUESTION # 26
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. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
C. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
D. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.

Answer: D

NEW QUESTION # 27
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