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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Management and Governance | 25% | - Data security and access control
|
| Data Pipeline Orchestration | 18% | - Pipeline automation and scheduling
|
| Data Preparation and Ingestion | 30% | - Storage solutions selection
|
| Data Analysis and Presentation | 27% | - Data exploration and analysis
|
Google Associate Data Practitioner Sample Questions:
1. 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) 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.
C) Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
D) Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
2. 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) Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
B) Use Vertex Al Workbench to develop a custom 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.
3. 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) Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
B) Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
C) Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
D) Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
4. 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 Admin role.
B) Request the Dataflow Developer role.
C) Request the Dataflow Viewer role.
D) Request the Dataflow Worker role.
5. Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?
A) Migrate the Spark jobs to Dataproc on Compute Engine.
B) Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
C) Migrate the Spark jobs to Dataproc Serverless.
D) Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: C |
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