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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Processing and Analytics | 20-30% | - Query and analyze datasets - Aggregate and summarize data - Apply statistical methods for analysis - Use BigQuery and SQL for analytics - Build and maintain data pipelines |
| Topic 2: Data Visualization and Insights | 20-30% | - Interpret and communicate findings - Present data insights to stakeholders - Build visualizations using Looker Studio - Choose appropriate visualization types - Create dashboards and reports |
| Topic 3: Data-Driven Decision Making | 10-20% | - Identify stakeholders and requirements - Assess data quality and completeness - Define success metrics - Translate business requirements into data solutions |
| Topic 4: Data Preparation and Exploration | 20-30% | - Transform and prepare data for analysis - Ingest and acquire data - Explore data through visualization and queries - Identify data quality issues - Perform exploratory data analysis (EDA) |
Google Associate Data Practitioner Sample Questions:
You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.
Which Google Cloud services should you recommend for the pipeline?
- A. Dataflow BigQuery
- B. BigQuery Analytics Hub
- C. Cloud Composer Cloud SQL for MySQL
- D. Dataproc Serverless Bigtable
Correct Answer: A 🗳️
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. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
- B. Migrate the Spark jobs to Dataproc Serverless.
- C. Migrate the Spark jobs to Dataproc on Compute Engine.
- D. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
Correct Answer: B 🗳️
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 Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
- B. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
- C. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
- D. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
Correct Answer: A 🗳️
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 BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
- D. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
Correct Answer: C 🗳️
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 BigQuery ML to create a logistic regression model for purchase prediction.
- B. Use Vertex Al Workbench to develop a custom model for purchase prediction.
- C. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
- D. Use Colab Enterprise to develop a custom model for purchase prediction.
Correct Answer: A 🗳️
