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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Pipeline Orchestration	18%	- 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 - Data transformation concepts 1. ETL vs ELT processes 2. Data transformation 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. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner 2. Storage location types: regional, dual-regional, multi-regional, zonal - Data formats and classification 1. Formats: CSV, JSON, Parquet, Avro, database tables 2. Structured, semi-structured, unstructured data
Data Management and Governance	25%	- Data security and access control 1. Data encryption and protection 2. IAM roles and permissions - 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 Analysis and Presentation	27%	- Business intelligence and decision support 1. Answer business questions with data 2. Communicate findings effectively - Data visualization and reporting 1. Looker dashboards and reports 2. Visualization best practices - 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
You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

A. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
B. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
C. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
D. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.

Answer: B

NEW QUESTION # 56
You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?

A. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
B. Grant each partner read access to the BigQuery dataset by using 1AM roles.
C. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
D. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.

Answer: C

NEW QUESTION # 57
Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner's data in its own BigQuery dataset. Each partner should be able to access only their dat a. You want to share the data while following Google-recommended practices. What should you do?

A. Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub /Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.
B. Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.
C. Grant the partners the bigquery.user IAM role on the BigQuery project.
D. Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.

Answer: B

NEW QUESTION # 58
Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

A. Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
B. Create a saved query in the BigQuery console that uses the SUM() function and the DATE_SUB() function. Re-run the saved query every month, and save the results to a BigQuery table.
C. Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
D. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.

Answer: A

NEW QUESTION # 59
Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

A. Use Dataform to define the transformations in SQLX.
B. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
C. Create BigQuery scheduled queries to define the transformations in SQL.
D. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.

Answer: A

NEW QUESTION # 60
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