Google ADP Questions Exam Study Tips And Information

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By doing this you can stay updated and competitive in the market and achieve your career objectives in a short time period. To do this you just need to pass the one Associate Data Practitioner (ADP) exam. Are you ready for this? If yes then enroll in Associate Data Practitioner (ADP) exam dumps and start this journey with ITPassLeader. The ITPassLeader offers real, valid, and updated ADP Questions that surely will help you in exam preparation and enable you to pass the challenging Associate Data Practitioner (ADP) exam with flying colors.

Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis and Presentation	27%	- Data exploration and analysis 1. BigQuery SQL queries 2. Jupyter notebooks for analysis 3. Identify trends, patterns, insights - Data visualization and reporting 1. Visualization best practices 2. Looker dashboards and reports - Business intelligence and decision support 1. Answer business questions with data 2. Communicate findings effectively
Topic 2: Data Preparation and Ingestion	30%	- 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. Cloud Data Fusion, Storage Transfer Service 2. Dataflow, BigQuery Data Transfer Service, Database Migration Service - Data formats and classification 1. Structured, semi-structured, unstructured data 2. Formats: CSV, JSON, Parquet, Avro, database tables
Topic 3: Data Management and Governance	25%	- Data quality and maintenance 1. Data validation and cleaning 2. Data lifecycle management - 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
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. Schedule and monitor data jobs 2. Error handling and monitoring

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Google Associate Data Practitioner Sample Questions (Q48-Q53):

NEW QUESTION # 48
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. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
B. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
C. 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.
D. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.

Answer: D

NEW QUESTION # 49
You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

A. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
B. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
C. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
D. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.

Answer: C

NEW QUESTION # 50
You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?

A. Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.
B. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
C. Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.
D. Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.

Answer: A

NEW QUESTION # 51
Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

A. Use Storage Transfer Service.
B. Use gcloud storage commands.
C. Use Cloud Storage FUSE.
D. Use a Transfer Appliance.

Answer: A

NEW QUESTION # 52
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. Create a view that filters out rows that are older than one year.
B. Require users to specify a partition filter using the alter table statement in SQL.
C. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
D. 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.

Answer: C

NEW QUESTION # 53
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