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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Analysis and Presentation | 27% | - Data visualization and reporting
|
| Topic 2: Data Management and Governance | 25% | - Compliance and governance
|
| Topic 3: Data Pipeline Orchestration | 18% | - Pipeline automation and scheduling
|
| Topic 4: Data Preparation and Ingestion | 30% | - Storage solutions selection
|
Google Associate Data Practitioner Sample Questions:
Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?
- A. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
- B. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
- C. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
- D. Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.
Correct Answer: B 🗳️
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. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
- B. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
- C. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
- D. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
Correct Answer: C 🗳️
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?
- A. Use Cloud Scheduler to schedule the jobs to run.
- B. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
- C. Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
- D. Use Cloud Tasks to schedule and run the jobs asynchronously.
Correct Answer: B 🗳️
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. Use Dataflow to transform the data and write the transformed data to BigQuery.
- B. Use Cloud Run functions to transform and load the data into BigQuery.
- C. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
- D. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
Correct Answer: A 🗳️
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. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
- 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. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
Correct Answer: C 🗳️
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