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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data-Driven Decision Making | 10-20% | - Translate business requirements into data solutions - Define success metrics - Assess data quality and completeness - Identify stakeholders and requirements |
| Topic 2: Data Preparation and Exploration | 20-30% | - Identify data quality issues - Perform exploratory data analysis (EDA) - Transform and prepare data for analysis - Explore data through visualization and queries - Ingest and acquire data |
| Topic 3: Data Processing and Analytics | 20-30% | - Apply statistical methods for analysis - Query and analyze datasets - Build and maintain data pipelines - Aggregate and summarize data - Use BigQuery and SQL for analytics |
| Topic 4: Data Visualization and Insights | 20-30% | - Create dashboards and reports - Build visualizations using Looker Studio - Choose appropriate visualization types - Present data insights to stakeholders - Interpret and communicate findings |
Google Associate Data Practitioner Sample Questions:
1. 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 Worker role.
B) Request the Dataflow Admin role.
C) Request the Dataflow Viewer role.
D) Request the Dataflow Developer role.
2. You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types.
You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?
A) Create a Cloud Run function to periodically check object metadata, and move objects to the appropriate storage class based on age and access patterns. Use object holds to enforce immutability for specific objects.
B) Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.
C) Move objects to different storage classes based on their age and access patterns. Use Cloud Key Management Service (Cloud KMS) to encrypt specific objects with customer-managed encryption keys (CMEK) to meet immutability requirements.
D) Configure lifecycle management rules to transition objects to appropriate storage classes based on access patterns. Set up Object Versioning for all objects to meet immutability requirements.
3. Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?
A) Change the bucket storage class to Archive.
B) Enable Object Versioning.
C) Set a bucket retention policy.
D) Set temporary object holds.
4. 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) Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
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 Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
D) Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
5. 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) Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
C) Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
D) Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: C | Question # 5 Answer: A |
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