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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation and Exploration	20-30%	- Perform exploratory data analysis (EDA) - Identify data quality issues - Transform and prepare data for analysis - Ingest and acquire data - Explore data through visualization and queries
Topic 2: Data Processing and Analytics	20-30%	- Build and maintain data pipelines - Use BigQuery and SQL for analytics - Aggregate and summarize data - Apply statistical methods for analysis - Query and analyze datasets
Topic 3: Data-Driven Decision Making	10-20%	- Define success metrics - Assess data quality and completeness - Identify stakeholders and requirements - Translate business requirements into data solutions
Topic 4: 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

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Google Associate Data Practitioner Sample Questions (Q60-Q65):

NEW QUESTION # 60
You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

A. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
B. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
C. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
D. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.

Answer: A

NEW QUESTION # 61
You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

A. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
B. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
C. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
D. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.

Answer: C

NEW QUESTION # 62
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 Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
B. Use Cloud Composer to orchestrate the Spark job and email the report.
C. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
D. Use Cloud Run functions to trigger the Spark job and email the report.

Answer: C

NEW QUESTION # 63
Your organization's website uses an on-premises MySQL as a backend database. You need to migrate the on-premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?

A. Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.
B. Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
C. Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.
D. Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.

Answer: B

NEW QUESTION # 64
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. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
C. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
D. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.

Answer: A

NEW QUESTION # 65
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