Exam ADP Experience - ADP Updated CBT

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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Pipeline Orchestration	18%	- Pipeline design and automation 1. Workflow orchestration concepts (e.g., Cloud Composer) 2. Dataflow pipelines
Data Preparation and Ingestion	30%	- Data ingestion into Google Cloud services 1. Streaming ingestion with Pub/Sub concepts 2. Cloud Storage ingestion patterns
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
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

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Google Associate Data Practitioner Sample Questions (Q60-Q65):

NEW QUESTION # 60
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 Run functions to trigger 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 Composer to orchestrate the Spark job and email the report.

Answer: C

NEW QUESTION # 61
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 Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
C. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
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: C

NEW QUESTION # 62
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 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.
B. 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.
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 the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.

Answer: C

NEW QUESTION # 63
You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

A. Use Cloud Client Libraries to transfer the data over the internet.
B. Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.
C. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
D. Use the gcloud storage command to transfer the data over the internet.

Answer: C

NEW QUESTION # 64
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 # 65
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