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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Pipeline Orchestration	18%	- Pipeline design and automation 1. Dataflow pipelines 2. Workflow orchestration concepts (e.g., Cloud Composer)
Topic 2: Data Preparation and Ingestion	30%	- Data ingestion into Google Cloud services 1. Cloud Storage ingestion patterns 2. Streaming ingestion with Pub/Sub concepts
Topic 3: Data Management	25%	- Data governance and security 1. IAM and access control 2. Data lifecycle management - Storage and data organization 1. BigQuery datasets and tables management 2. Cloud Storage lifecycle policies
Topic 4: Data Analysis and Presentation	27%	- Querying and analyzing data 1. BigQuery SQL usage 2. Data aggregation and transformation - Data visualization 1. Looker / Looker Studio reporting

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Google Associate Data Practitioner Sample Questions (Q62-Q67):

NEW QUESTION # 62
You are storing data in Cloud Storage for a machine learning project. The data is frequently accessed during the model training phase, minimally accessed after 30 days, and unlikely to be accessed after 90 days. You need to choose the appropriate storage class for the different stages of the project to minimize cost. What should you do?

A. Store the data in Standard storage during the model training phase. Transition the data to Durable Reduced Availability (DRA) storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
B. Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
C. Store the data in Nearline storage during the model training phase. Transition the data to Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
D. Store the data in Nearline storage during the model training phase. Transition the data to Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.

Answer: B

NEW QUESTION # 63
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 Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
C. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
D. Use Cloud Run functions to trigger the Spark job and email the report.

Answer: C

NEW QUESTION # 64
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 the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
B. 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.
C. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
D. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.

Answer: D

NEW QUESTION # 65
Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

A. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
B. Migrate the Spark jobs to Dataproc Serverless.
C. Migrate the Spark jobs to Dataproc on Compute Engine.
D. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.

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 a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
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 Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.

Answer: B

NEW QUESTION # 67
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