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Google ADP Exam Syllabus Topics:

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

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Both practice tests simulate the Google ADP real exam environment and produce results of your attempts on the spot. In this way, you will be able to not only evaluate your progress but also overcome mistakes before the ADP actual examination. Windows computers support the Associate Data Practitioner ADP desktop practice exam software. The Associate Data Practitioner ADP web-based practice test needs an active internet connection.

Google Associate Data Practitioner Sample Questions (Q43-Q48):

NEW QUESTION # 43
You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage.
The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

A. ETLT
B. EL
C. ETL
D. ELT

Answer: C

NEW QUESTION # 44
You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types.
You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?

A. Configure lifecycle management rules to transition objects to appropriate storage classes based on access patterns. Set up Object Versioning for all objects to meet immutability requirements.
B. Create a Cloud Run function to periodically check object metadata, and move objects to the appropriate storage class based on age and access patterns. Use object holds to enforce immutability for specific objects.
C. Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.
D. Move objects to different storage classes based on their age and access patterns. Use Cloud Key Management Service (Cloud KMS) to encrypt specific objects with customer-managed encryption keys (CMEK) to meet immutability requirements.

Answer: C

NEW QUESTION # 45
You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

A. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
B. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.
C. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
D. Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.

Answer: D

NEW QUESTION # 46
Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

A. Use Cloud Composer operators.
B. Use Dataform workflows.
C. Use Dataflow pipelines.
D. Use Cloud Data Fusion pipelines.

Answer: B

NEW QUESTION # 47
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 Dataproc workflow templates to define and schedule the Spark job, and to email the report.
B. Use Cloud Composer to orchestrate the Spark job and email the report.
C. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
D. Use Cloud Run functions to trigger the Spark job and email the report.

Answer: A

NEW QUESTION # 48
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