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It is a lifetime study time. The society advocates us to further our study and improve working skills at every aspect. For exam candidates like you it is of great importance to pass the Google exams effectively. That is why we offer you the excellent ADP learning materials: Associate Data Practitioner compiled by professional experts. Now, let us take a comprehensive look of the features of the ADP actual lab questions as follow:
Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data-Driven Decision Making | 10-20% | - Assess data quality and completeness - Define success metrics - Translate business requirements into data solutions - Identify stakeholders and requirements |
| Data Preparation and Exploration | 20-30% | - Transform and prepare data for analysis - Ingest and acquire data - Perform exploratory data analysis (EDA) - Identify data quality issues - Explore data through visualization and queries |
| Data Processing and Analytics | 20-30% | - Apply statistical methods for analysis - Aggregate and summarize data - Use BigQuery and SQL for analytics - Query and analyze datasets - Build and maintain data pipelines |
| Data Visualization and Insights | 20-30% | - Present data insights to stakeholders - Interpret and communicate findings - Build visualizations using Looker Studio - Choose appropriate visualization types - Create dashboards and reports |
Google Associate Data Practitioner Sample Questions:
Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?
- A. Use Dataform to define the transformations in SQLX.
- B. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.
- C. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
- D. Create BigQuery scheduled queries to define the transformations in SQL.
Correct Answer: A 🗳️
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?
- A. Use Vertex Al Workbench to develop a custom model for purchase prediction.
- B. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
- C. Use Colab Enterprise to develop a custom model for purchase prediction.
- D. Use BigQuery ML to create a logistic regression model for purchase prediction.
Correct Answer: D 🗳️
You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?
- A. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
- B. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
- C. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
- D. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
Correct Answer: D 🗳️
You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?
- A. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
- B. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
- C. Use Dataflow to transform the data and write the transformed data to BigQuery.
- D. Use Cloud Run functions to transform and load the data into BigQuery.
Correct Answer: C 🗳️
You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege. What should you do?
- A. Request the Dataflow Viewer role.
- B. Request the Dataflow Worker role.
- C. Request the Dataflow Admin role.
- D. Request the Dataflow Developer role.
Correct Answer: D 🗳️
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