Test ADP Centres - Exam Sample ADP Questions

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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 2: Data Visualization and Insights	20-30%	- Choose appropriate visualization types - Interpret and communicate findings - Build visualizations using Looker Studio - Create dashboards and reports - Present data insights to stakeholders
Topic 3: Data Processing and Analytics	20-30%	- Use BigQuery and SQL for analytics - Query and analyze datasets - Build and maintain data pipelines - Apply statistical methods for analysis - Aggregate and summarize data
Topic 4: Data Preparation and Exploration	20-30%	- Identify data quality issues - Transform and prepare data for analysis - Perform exploratory data analysis (EDA) - Explore data through visualization and queries - Ingest and acquire data

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Exam Sample Google ADP Questions & Latest ADP Cram Materials

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Google Associate Data Practitioner Sample Questions (Q24-Q29):

NEW QUESTION # 24
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 existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
B. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
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.

Answer: A

NEW QUESTION # 25
Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

A. Require users to specify a partition filter using the alter table statement in SQL.
B. Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
C. Create a view that filters out rows that are older than one year.
D. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.

Answer: D

NEW QUESTION # 26
You are storing data in Cloud Storage for a machine learning project. The data is frequently accessed during the model training phase, minimally accessed after 30 days, and unlikely to be accessed after 90 days. You need to choose the appropriate storage class for the different stages of the project to minimize cost. What should you do?

A. Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
B. Store the data in Nearline storage during the model training phase. Transition the data to Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.
C. Store the data in Nearline storage during the model training phase. Transition the data to Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
D. Store the data in Standard storage during the model training phase. Transition the data to Durable Reduced Availability (DRA) storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

Answer: A

NEW QUESTION # 27
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 Dataflow to create a streaming pipeline that includes validation and transformation steps.
B. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
C. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
D. 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.

Answer: A

NEW QUESTION # 28
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 Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
B. Use Dataflow to transform the data and write the transformed data to BigQuery.
C. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
D. Use Cloud Run functions to transform and load the data into BigQuery.

Answer: B

NEW QUESTION # 29
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