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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation and Ingestion	30%	- Data formats and classification 1. Structured, semi-structured, unstructured data 2. Formats: CSV, JSON, Parquet, Avro, database tables - Data loading methods 1. Batch and streaming ingestion 2. gcloud, BQ CLI, client libraries - 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 2: Data Management and Governance	25%	- Compliance and governance 1. Data privacy and regulatory requirements 2. Data stewardship and cataloging - 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
Topic 3: Data Pipeline Orchestration	18%	- 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 transformation concepts 1. Data transformation requirements 2. ETL vs ELT processes
Topic 4: Data Analysis and Presentation	27%	- Data visualization and reporting 1. Looker dashboards and reports 2. Visualization best practices - Business intelligence and decision support 1. Communicate findings effectively 2. Answer business questions with data - Data exploration and analysis 1. Jupyter notebooks for analysis 2. Identify trends, patterns, insights 3. BigQuery SQL queries

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Google Associate Data Practitioner Sample Questions (Q63-Q68):

NEW QUESTION # 63
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 Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
B. Create BigQuery scheduled queries to define the transformations in SQL.
C. Use Dataform to define the transformations in SQLX.
D. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.

Answer: C

NEW QUESTION # 64
Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

A. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
B. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
C. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
D. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.

Answer: B

NEW QUESTION # 65
You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?

A. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
B. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
C. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
D. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.

Answer: B

NEW QUESTION # 66
You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

A. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
B. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
C. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
D. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.

Answer: B

NEW QUESTION # 67
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 Composer to orchestrate the Spark job and email the report.
B. Use Cloud Run functions to trigger 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 Dataproc workflow templates to define and schedule the Spark job, and to email the report.

Answer: D

NEW QUESTION # 68
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