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Google ADP Exam Syllabus Topics:

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

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Google - ADP - High Hit-Rate Online Associate Data Practitioner Tests

Candidates who pass ADP Certification prove their worth in the Google field. The Associate Data Practitioner certification is proof of their competence and skill. This skill is highly useful in big Google companies that facilitate a candidate's career. To get certified, it is very important that you pass the Associate Data Practitioner certification exam to prove your skills to the tech company. For this task, you require high-quality and accurate prep material to help you out. And many people don't get reliable material and ultimately fail. Failure leads to a loss of time and money.

Google Associate Data Practitioner Sample Questions (Q63-Q68):

NEW QUESTION # 63
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?

A. Apply a filter to only show products with a positive profit margin.
B. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
C. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
D. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.

Answer: B

NEW QUESTION # 64
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 Vertex Al Workbench to develop a custom model for purchase prediction.
B. Use Colab Enterprise 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 BigQuery ML to create a logistic regression model for purchase prediction.

Answer: D

NEW QUESTION # 65
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. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
C. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
D. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.

Answer: C

NEW QUESTION # 66
You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

A. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
B. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
C. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
D. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.

Answer: A

NEW QUESTION # 67
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 Durable Reduced Availability (DRA) 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 Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
C. 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.
D. 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.

Answer: C

NEW QUESTION # 68
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