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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis and Presentation	27%	- Data visualization and reporting 1. Visualization best practices 2. Looker dashboards and reports - Business intelligence and decision support 1. Communicate findings effectively 2. Answer business questions with data - Data exploration and analysis 1. BigQuery SQL queries 2. Identify trends, patterns, insights 3. Jupyter notebooks for analysis
Topic 2: Data Management and Governance	25%	- Data security and access control 1. Data encryption and protection 2. IAM roles and permissions - Data quality and maintenance 1. Data lifecycle management 2. Data validation and cleaning - Compliance and governance 1. Data privacy and regulatory requirements 2. Data stewardship and cataloging
Topic 3: Data Preparation and Ingestion	30%	- Data loading methods 1. Batch and streaming ingestion 2. gcloud, BQ CLI, client libraries - Storage solutions selection 1. Storage location types: regional, dual-regional, multi-regional, zonal 2. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner - Data extraction and transfer tools 1. Cloud Data Fusion, Storage Transfer Service 2. Dataflow, BigQuery Data Transfer Service, Database Migration Service - Data formats and classification 1. Formats: CSV, JSON, Parquet, Avro, database tables 2. Structured, semi-structured, unstructured data
Topic 4: Data Pipeline Orchestration	18%	- Transformation tools selection 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform - Data transformation concepts 1. Data transformation requirements 2. ETL vs ELT processes - Pipeline automation and scheduling 1. Error handling and monitoring 2. Schedule and monitor data jobs

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Google ADP Reliable Test Topics, ADP Sample Questions Answers

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Google Associate Data Practitioner Sample Questions (Q53-Q58):

NEW QUESTION # 53
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. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
B. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
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.

Answer: C

NEW QUESTION # 54
Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

A. Store the data in Cloud Storage using Archive storage.
B. Store the data in Cloud Storage using Nearline storage.
C. Store the data in Cloud Storage using Standard storage.
D. Store the data in Cloud Storage using Coldline storage.

Answer: B

NEW QUESTION # 55
Your company uses Looker to generate and share reports with various stakeholders. You have a complex dashboard with several visualizations that needs to be delivered to specific stakeholders on a recurring basis, with customized filters applied for each recipient. You need an efficient and scalable solution to automate the delivery of this customized dashboard. You want to follow the Google- recommended approach. What should you do?

A. Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
B. Use the Looker Scheduler with a user attribute filter on the dashboard, and send the dashboard with personalized filters to each stakeholder based on their attributes.
C. Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
D. Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.

Answer: B

NEW QUESTION # 56
You have a BigQuery dataset containing sales dat
a. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

A. Store all data in a single BigQuery table without partitioning or lifecycle policies.
B. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
C. Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.
D. Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.

Answer: D

NEW QUESTION # 57
Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

A. Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
B. Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
C. Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.
D. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.

Answer: C

NEW QUESTION # 58
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