Google certification ADP the latest examination questions and answers come out

According to personal propensity and various understanding level of exam candidates, we have three versions of ADP practice materials for your reference. Here are the respective features and detailed disparities of our ADP practice materials. Pdf version- it is legible to read and remember, and support customers’ printing request, so you can have a print and practice in papers. Software version-It support simulation test system, and times of setup has no restriction. Remember this version support Windows system users only. App online version-Be suitable to all kinds of equipment or digital devices. Be supportive to offline exercise on the condition that you practice it without mobile data.

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. Identify trends, patterns, insights 2. Jupyter notebooks for analysis 3. BigQuery SQL queries
Topic 2: Data Management and Governance	25%	- Compliance and governance 1. Data stewardship and cataloging 2. Data privacy and regulatory requirements - Data security and access control 1. Data encryption and protection 2. IAM roles and permissions - Data quality and maintenance 1. Data validation and cleaning 2. Data lifecycle management
Topic 3: Data Preparation and Ingestion	30%	- Data formats and classification 1. Formats: CSV, JSON, Parquet, Avro, database tables 2. Structured, semi-structured, unstructured data - Data loading methods 1. gcloud, BQ CLI, client libraries 2. Batch and streaming ingestion - 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. Dataflow, BigQuery Data Transfer Service, Database Migration Service 2. Cloud Data Fusion, Storage Transfer Service
Topic 4: 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. Schedule and monitor data jobs 2. Error handling and monitoring

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Google Associate Data Practitioner Sample Questions (Q20-Q25):

NEW QUESTION # 20
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. 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.
B. Store all data in a single BigQuery table without partitioning or lifecycle policies.
C. 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.
D. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.

Answer: C

NEW QUESTION # 21
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. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
B. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
C. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
D. Apply a filter to only show products with a positive profit margin.

Answer: C

NEW QUESTION # 22
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. 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.
C. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
D. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.

Answer: A

NEW QUESTION # 23
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 Admin role.
B. Request the Dataflow Developer role.
C. Request the Dataflow Worker role.
D. Request the Dataflow Viewer role.

Answer: B

NEW QUESTION # 24
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

A. Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
B. Use Cloud Tasks to schedule and run the jobs asynchronously.
C. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
D. Use Cloud Scheduler to schedule the jobs to run.

Answer: C

NEW QUESTION # 25
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