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Google ADP Exam Syllabus Topics:

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

>> Google ADP Minimum Pass Score <<

ADP exam collection: Associate Data Practitioner & ADP torrent VCE

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Google Associate Data Practitioner Sample Questions (Q20-Q25):

NEW QUESTION # 20
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

A. Use Vertex Al Workbench to develop a custom model for purchase prediction.
B. Use Colab Enterprise to develop a custom model for purchase prediction.
C. Use BigQuery ML to create a logistic regression model for purchase prediction.
D. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.

Answer: C

NEW QUESTION # 21
Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?

A. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
B. Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.
C. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
D. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.

Answer: A

NEW QUESTION # 22
You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?

A. Evaluate the data skewness.
B. Evaluate data drift.
C. Compare the two different models.
D. Compare the confusion matrix.

Answer: B

NEW QUESTION # 23
You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

A. Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
B. Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.
C. Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
D. Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.

Answer: C

NEW QUESTION # 24
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 BigQuery subscription in Pub/Sub.
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 Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
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 # 25
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