Google Data Engineer : GCP-DE

여러분은 열악한 취업환경속에서 치열한 경쟁을 많이 느낄것입니다. 치열한 경쟁속에서 자신의 위치를 보장하는 길은 더 많이 배우고 더 많이 노력하는것 뿐입니다.

Google GCP-DE 시험은 국제인증자격증중에서 뜨거운 인기를 누리고 있습니다. Pass4Test는 국제인증자격증 시험에 대비한 Data Engineer시험전 공부자료를 제공해드리는 전문적인 사이트입니다.한방에 쉽게 Data Engineer시험에서 고득점으로 패스하고 싶다면 Data Engineer시험자료를 선택하세요.저렴한 가격에 비해 너무나도 높은 시험적중율과 시험패스율을 자랑하는 Google GCP-DE덤프를 제작하기 위해 최선을 다하고 있습니다.

Pass4Test에서 제공해드리는 덤프와의 근사한 만남이 Data Engineer 최신 시험패스에 화이팅을 불러드립니다. 덤프에 있는 문제만 공부하면 되기에 시험일이 며칠뒤라도 시험패스는 문제없습니다. 더는 공부하지 않은 자신을 원망하지 마시고 결단성있게 Data Engineer최신덤프로 시험패스에 고고싱하세요.

덤프는 구체적인 업데이트 주기가 존재하지 않습니다. 하지만 저희는 수시로 Google GCP-DE시험문제 변경을 체크하여 Data Engineer덤프를 가장 최신버전으로 업데이트하도록 최선을 다하고 있습니다. 덤프가 업데이트되면 업데이트된 최신버전을 고객님 구매시 사용한 메일주소로 발송해드립니다. Google GCP-DE자료를 구매하신후 60일내로 불합격받고 환불신청하시면 덤프결제를 취소해드립니다.

구매후 GCP-DE덤프를 바로 다운: 결제하시면 시스템 자동으로 구매한 제품을 고객님 메일주소에 발송해드립니다.(만약 12시간이내에 덤프를 받지 못하셨다면 연락주세요.주의사항:스펨메일함도 꼭 확인해보세요.)

최신 Google Cloud Certified GCP-DE 무료샘플문제:

1. You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A) Use BigQuery UPDATE to further reduce the size of the dataset.
B) Preserve the structure of the data as much as possible.
C) Denormalize the data as must as possible.
D) Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
E) Develop a data pipeline where status updates are appended to BigQuery instead of updated.
F) Use BigQuery'ssupport for external data sources to query.

2. You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A) Create a cron schedule in Cloud Dataprep.
B) Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
C) Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
D) Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.

3. You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A) Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
B) Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
C) Write an Apache Beam pipeline that creates a BigQuery table per data
D) Re-create the tables using DD
E) Partition the tables by a column containing a TIMESTAMP or DATE Type.
F) Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

4. Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A) If a preemptible worker is reclaimed, then a replacement worker must be added manually.
B) A Dataproc cluster cannot have only preemptible workers.
C) Preemptible workers cannot use persistent disk.
D) Preemptible workers cannot store data.

5. You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

A) Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
B) Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
C) Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
D) Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
E) If that number falls below 4000, send an alert.
F) Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
G) Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
H) Consume the stream of data in Cloud Dataflow using Kafka I
I) Consume the stream of data in Cloud Dataflow using Kafka I
J) Set a sliding time window of 1 hour every 5 minute
K) Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
L) If that number falls below 4000, send an alert.
M) Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

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