Authorized ADP Certification | ADP Official Study Guide

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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q55-Q60):

NEW QUESTION # 55
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. Migrate the Spark jobs to Dataproc Serverless.
B. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
C. Migrate the Spark jobs to Dataproc on Compute Engine.
D. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.

Answer: A

NEW QUESTION # 56
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 Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
B. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
C. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
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: B

NEW QUESTION # 57
You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

A. Export the data to Cloud Storage, and use signed URLs to authorize access.
B. Update dataset privileges by using the SQL GRANT statement.
C. Encrypt the data by using customer-managed encryption keys (CMEK).
D. Enable access control by using IAM roles.

Answer: D

NEW QUESTION # 58
You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

A. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
B. Use Cloud Composer to orchestrate the Spark job and email the report.
C. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
D. Use Cloud Run functions to trigger the Spark job and email the report.

Answer: C

NEW QUESTION # 59
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. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
B. Use Vertex Al Workbench to develop a custom model for purchase prediction.
C. Use BigQuery ML to create a logistic regression model for purchase prediction.
D. Use Colab Enterprise to develop a custom model for purchase prediction.

Answer: C

NEW QUESTION # 60
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