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Professional-Data-Engineer Test Guide, Professional-Data-Engineer Testdump

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Google Certified Professional Data Engineer Exam Sample Questions (Q59-Q64):

NEW QUESTION # 59
You want to rebuild your batch pipeline for structured data on Google Cloud You are using PySpark to conduct data transformations at scale, but your pipelines are taking over twelve hours to run To expedite development and pipeline run time, you want to use a serverless tool and SQL syntax You have already moved your raw data into Cloud Storage How should you build the pipeline on Google Cloud while meeting speed and processing requirements?

A. Use Apache Beam Python SDK to build the transformation pipelines, and write the data into BigQuery
B. Ingest your data into Cloud SQL, convert your PySpark commands into SparkSQL queries to transform the data, and then use federated queries from BigQuery for machine learning.
C. Ingest your data into BigQuery from Cloud Storage, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table
D. Convert your PySpark commands into SparkSQL queries to transform the data; and then run your pipeline on Dataproc to write the data into BigQuery

Answer: D

NEW QUESTION # 60
You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available.
How should you use this data to train the model?

A. Continuously retrain the model on a combination of existing data and the new data.
B. Train on the new data while using the existing data as your test set.
C. Train on the existing data while using the new data as your test set.
D. Continuously retrain the model on just the new data.

Answer: C

Explanation:
https://cloud.google.com/automl-tables/docs/prepare

NEW QUESTION # 61
Which of these is not a supported method of putting data into a partitioned table?

A. Create a partitioned table and stream new records to it every day.
B. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
C. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
D. Use ORDER BY to put a table's rows into chronological order and then change the table's type to
"Partitioned".

Answer: D

Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

NEW QUESTION # 62
You are migrating a large number of files from a public HTTPS endpoint to Cloud Storage. The files are protected from unauthorized access using signed URLs. You created a TSV file that contains the list of object URLs and started a transfer job by using Storage Transfer Service. You notice that the job has run for a long time and eventually failed Checking the logs of the transfer job reveals that the job was running fine until one point, and then it failed due to HTTP 403 errors on the remaining files You verified that there were no changes to the source system You need to fix the problem to resume the migration process. What should you do?

A. Update the file checksums in the TSV file from using MD5 to SHA256. Remove the completed files from the TSV file and rerun the Storage Transfer Service job.
B. Create a new TSV file for the remaining files by generating signed URLs with a longer validity period.Split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel.
C. Renew the TLS certificate of the HTTPS endpoint Remove the completed files from the TSV file and rerun the Storage Transfer Service job.
D. Set up Cloud Storage FUSE, and mount the Cloud Storage bucket on a Compute Engine Instance Remove the completed files from the TSV file Use a shell script to iterate through the TSV file and download the remaining URLs to the FUSE mount point.

Answer: B

Explanation:
A signed URL is a URL that provides limited permission and time to access a resource on a web server. It is often used to grant temporary access to protected files without requiring authentication. Storage Transfer Service is a service that allows you to transfer data from external sources, such as HTTPS endpoints, to Cloud Storage buckets. You can use a TSV file to specify the list of URLs to transfer. In this scenario, the most likely cause of the HTTP 403 errors is that the signed URLs have expired before the transfer job could complete. This could happen if the signed URLs have a short validity period or the transfer job takes a long time due to the large number of files or network latency. To fix the problem, you need to create a new TSV file for the remaining files by generating new signed URLs with a longer validity period. This will ensure that the URLs do not expire before the transfer job finishes. You can use the Cloud Storage tools or your own program to generate signed URLs. Additionally, you can split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel. This will speed up the transfer process and reduce the risk of errors. References:
* Signed URLs | Cloud Storage Documentation
* V4 signing process with Cloud Storage tools
* V4 signing process withyour own program
* Using a URL list file
* What Is a 403 Forbidden Error (and How Can I Fix It)?

NEW QUESTION # 63
You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

A. You expect future mutations to have different features from the mutated samples in the database.
B. There are very few occurrences of mutations relative to normal samples.
C. There are roughly equal occurrences of both normal and mutated samples in the database.
D. You expect future mutations to have similar features to the mutated samples in the database.
E. You already have labels for which samples are mutated and which are normal in the database.

Answer: B,D

Explanation:
Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. https://en.wikipedia.org/wiki/Anomaly_detection

NEW QUESTION # 64
......

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